882 results about "Diagnostic model" patented technology

Methods are provided for predicting the presence, subtype and stage of ovarian cancer, as well as for assessing the therapeutic efficacy of a cancer treatment and determining whether a subject potentially is developing cancer. Associated test kits, computer and analytical systems as well as software and diagnostic models are also provided.

A method for troubleshooting a problem with a device includes acquiring device data for the device, receiving a user's query concerning a device in a natural language, presenting possible refinements to at least a portion of the user's query for defining a problem statement, presenting candidate solutions that are associated with the defined problem statement in a knowledge base, at least one of the presentation of possible refinements and the presentation of candidate solutions being informed by device data that is linked through a diagnostic model of the device to at least one of the problem statements and candidate solutions.

A diagnostic system and method for enabling multistage decision optimization in aircraft preflight dispatch. The diagnostic system includes an interface for receiving one or more inputs relating to one or more observed symptoms indicative of a failed component in an aircraft. The diagnostic system extends an entropy-based value of information (VOI) diagnostic model by adding an explicit value function into the VOI diagnostic model to accommodate various variables associated with the aircraft preflight dispatch problem. The construction of the entropy-based VOI diagnostic model and thus the extended VOI diagnostic model are both based upon at least one of systemic information relating to aircraft components and input-output relationships of the aircraft components, experience-based information relating to direct relationships between aircraft component failures and observed symptoms, and factual information relating to aircraft component reliability.

The invention discloses a fault diagnosis method of a high-voltage circuit breaker based on a depth belief network, which comprises the following steps: step 1, selecting a data sample required by anexperiment, and dividing the unified standardized sample data into a test sample and a training sample according to a specific proportion; Step 2: building and initializing the DBN deep belief networkfault diagnosis model; Step 3, inputting a large number of unlabeled samples or unlabeled samples in the pre-training set from the bottom of the model, and pre-training the RBM in the model by usinglayer-by-layer unsupervised greedy learning; Step 4: the whole model being fine-tuned by genetic algorithm; Step 5, the fault diagnosis model of the high-voltage circuit breaker obtained by training being classified to the fault samples of the test set in step 1, so as to obtain the fault classification result, and the diagnosis accuracy rate of the model being counted. The invention discloses a fault diagnosis method of a high-voltage circuit breaker based on a depth belief network, which can train a large amount of data samples to realize the fault diagnosis function of the high-voltage circuit breaker.

The invention discloses a construction method and a construction system for a clinical diagnosis model, and a clinical diagnosis system, and relates to the technical field of medical treatment. The construction method for a clinical diagnosis model comprises: step S1, acquiring data, data acquisition sources being a plurality of electronic medical records; step S2, processing the acquired data; step S3, according to known typical medical history in the processed data, establishing a case analysis model, and training and evaluating the case analysis model; step S4, applying the trained and evaluated model in the clinical diagnosis system, to form a the clinical diagnosis model. The method and the systems are used to improve accuracy for a doctor to diagnose diseases.

The invention discloses a method for constructing a cardiovascular disease diagnosis model, a system and the diagnosis model. The cascade classifier is trained to get the ear detection model. VGG, GoogleNet and ResNet neural network models are used to extract ear features. The spatial pyramid is used to integrate the features of ear extracted from neural network model, and the depth heterogeneousfeature map of each neural network model is obtained. Feature preprocessing of depth isomerism feature map; Training to obtain SVM classifier model; The SVM classifier model and the three trained neural network models are integrated by Bagging learning to obtain the cardiovascular disease diagnosis model. The cardiovascular disease diagnosis model constructed by the invention can comprehensively and scientifically carry out cardiovascular disease diagnosis and prediction, has high precision, and can be widely applied in the field of automatic processing of medical data.

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.

The invention discloses a rolling bearing fault diagnosis method based on improved variational model decomposition and an extreme learning machine. The method comprises: vibration signals of a rollingbearing under different types of faults are collected, the vibration signals are filtered by means of maximum correlation kurtosis deconvolution, parameter optimization is carried out on the maximumcorrelation kurtosis deconvolution method by using a particle swarm algorithm, and an enveloped energy entropy after signal deconvolution is used as a fitness function; the mode number of variationalmodel decomposition is improved by an energy threshold and improved variational model decomposition of the filtered vibration signals is realized to obtain mode matrixes of the corresponding vibrationsignals; singular value decomposition is carried out on the mode matrixes to obtain a singular value vector and a rolling bearing fault feature set is constructed; and the fault feature set is trained by using an extreme learning machine and a rolling bearing fault diagnosis model is established. Therefore, stable feature extraction of the complex vibration signal of the rolling bearing is realized, so that the diagnostic accuracy is improved.

The invention relates to a computer vision-based rice disease, pest and weed diagnostic method. The method comprises the following steps: collecting different varieties of rice leaf disease samples at different morbidity periods in different growth environments, and letting a plant disease expert identify the diseases infecting all the samples; then acquiring images of disease symptoms of the samples; realizing the automatic segmentation of disease spots by utilizing an image processing technology; extracting all characteristic indexes of the disease spots, and establishing a characteristic database corresponding to the diseases; on the basis, selecting proper disease spot characteristic parameters and an effective pattern recognition algorithm so as to establish a machine vision-based rice disease, pest and weed real-time diagnostic model.

An apparatus and a method for diagnosis are provided. The apparatus for diagnosis lesion include: a model generation unit configured to categorize learning data into one or more categories and to generate one or more categorized diagnostic models based on the categorized learning data, a model selection unit configured to select one or more diagnostic model for diagnosing a lesion from the categorized diagnostic models, and a diagnosis unit configured to diagnose the lesion based on image data of the lesion and the selected one or more diagnostic model.

The invention provides a lead-acid power battery system fault diagnosis method. The method involves an off-line part and an on-line part. The method includes the specific steps that in the off-line state, data are collected through a simulation model, the data are preprocessed by using a normalization method, a data classification training set and a testing set of a power battery system of a support vector machine are obtained, parameter adaptive optimization is conducted through a GA algorithm, a one-to-one method is used for training to obtain a diagnostic model of the support vector machine, and SVM decision classification is conducted; in the on-line state, a fault generating device is used for simulating fault signals, the signals are collected through a collection module, the data are preprocessed by using the normalization method, the data are further input into an SVM module in off-line training, and fault online classification based on an SVM algorithm is conducted. According to the lead-acid power battery system fault diagnosis method, intelligent off-line and on-line diagnosis of faults of the battery system can be achieved, and meanwhile the fault diagnosis recognition rate is increased.

The invention provides a mechanical equipment intelligent fault diagnosis method based on a partial migration convolutional network. The method comprises: collecting operation data of mechanical equipment under different operation working conditions; constituting data sets, taking part of data in the data set X as a source domain training sample set and a target domain test sample set; performingdata standardization on each piece of sample data; training two one-dimensional convolutional neural network models with the same structure and different initialization parameters by using the sourcedomain training sample set, and correcting the two trained convolutional neural network models based on the target domain test sample set to obtain a convolutional neural network mechanical equipmentfault diagnosis model; and performing fault diagnosis on the mechanical equipment based on the real-time operation data by using the fault diagnosis model to output a fault type. The method can be effectively used in more real mechanical fault diagnosis, that is to say, the label-free property of the target domain is considered, so that the trained diagnosis model can better diagnose faults of mechanical equipment.

The present invention provides an adjuvant disease diagnosis method based on patient test results. The method comprises: cleaning original data, and constructing sample data according to the test category; using the current xgboost framework with high speed and high accuracy to improve the algorithm and reduce the error rate, designing the model loss function and optimizing the number of iterations of the model, and training the multiple diagnostic models according to the type of the sample; and outputting some diseases with relatively large probability that patients could be suffered from. The present invention provides the disease diagnosis method with high reliability for assisting doctors to improve the diagnosis rate of the disease and reduce the misdiagnosis rate.

A method for diagnosing faults in a particular device within a fleet of devices is provided. The method comprises receiving performance data related to one or more parameters associated with a fleet of devices and processing the performance data to detect one or more trend shifts in the one or more parameters. The method then comprises detrending the one or more parameters to derive noise-adjusted performance data related to a particular parameter associated with a particular device. The method further comprises generating a fleet-based diagnostic model based on trend patterns and data characteristics associated with the fleet of devices. The fleet-based diagnostic model comprises one or more fuzzy rules defining one or more expected trend shift data ranges for the one or more parameters associated with the fleet of devices. The method then comprises computing one or more scaling factors for the particular parameter associated with the particular device and scaling the one or more of fuzzy rules defined for the one or more parameters in the fleet-based diagnostic model, based on the one or more scaling factors, to generate a personalized diagnostic model for the particular parameter associated with the particular device. The method finally comprises evaluating the personalized diagnostic model against the one or more trend shifts detected for the one or more parameters, to diagnose a fault associated with the particular device.

The invention provides a power grid fault diagnostic model and a diagnostic method thereof and belongs to the technical field of power grid fault diagnosis. A mathematical expression of the traditional fault diagnostic analytic model can be abstracted as the formula; analysis on the action state of protecting a switch is converted into description on the probability of protecting the switch; the description is information transmission uncertainty description based on an information theory; an objective function is established as an optimal solving function; mutual information between an information sink and an information source under every failure mode is calculated when multiple optimal solutions, namely multiple failure modes exist; corresponding failure modes are most likely to occur if the quantity of condition self-information is smallest; and a principle that the mutual information of the information sink and the information source is maximum is utilized to determine a fault sorting result when the plurality of condition self-information is similar. The power grid fault diagnostic model and the diagnostic method thereof have the advantages of enabling the uncertainty of fault diagnosis to be integrated in an analytic model, enabling the fault tolerance of the model to be improved and the dimension of the model to be greatly reduced, being high in diagnostic speed and diagnostic accuracy, being capable of being well applied to a scheduling terminal and playing a positive and important role in the field of the power grid fault diagnosis.