47 results about "Time to diagnosis" patented technology

The invention discloses an industrial process fault diagnosis method based on multiple classifiers and D-S evidence fusion. The method comprises the steps that firstly, independent repeated sampling is conducted according to fault data in the industrial process; secondly, the multiple classifiers are applied to new training data, respective off-line modeling models are obtained, and meanwhile the properties of all the classifiers are represented in the form of a fusion matrix; thirdly, different types of elementary probability valuation functions are calculated according to the D-S evidence theory, decisions of the multiple classifiers are selectively integrated and synthesized according to the similarity index, a combined elementary probability valuation function is obtained, and a final classified diagnosis result is obtained by means of comparison. Compared with other methods in the prior art, the industrial process fault diagnosis method can greatly improve the diagnosis effect of the industrial process, shorten delayed diagnosis time and increase the diagnosis accuracy rate, improves the monitoring performance to a great extent, enhances the comprehension ability and operation confidence of process operators in the process, and is more beneficial to automatic implementation of the industrial process.

The invention discloses a failure prediction diagnosis algorithm of a wind turbine generator gear box based on kernel principle component analysis (KPCA) and a support vector machine. Based on full consideration of indexes of temperature change of all elements when a failure occurs on a gear box and output power change before and after the failure occurs, the KPCA algorithm is adopted to reduce an input dimension for characteristic extracting, irrelevant data is abandoned, the model training speed can be drastically increased, and the failure diagnosis time can be reduced. Meanwhile, the support vector machine is introduced to conduct classified training on the data so as to improve the generalization ability; moreover, with the help of an expert system, the result is analyzed and explained, accurate and detailed information can be provided for a human-computer interaction interface, and thus precise diagnosis of the failure is achieved.

Infusion systems, infusion devices, and related operating methods are provided. An exemplary method of operating an infusion device to deliver fluid to a user in accordance with an operating mode involves obtaining operational information pertaining to one or more prior instances of the operating mode, obtaining status information pertaining to the infusion device, and determining a diagnosis time based at least in part on the operational information. The diagnosis time is prior to a subsequent instance of the operating mode. At the diagnosis time, the method automatically determines the viability of the subsequent instance of the operating mode based at least in part on the status information and automatically generates a notification indicative of a recommended action for the user based at least in part on the viability.

This invention is to provide a technique to appropriately authenticate the requesting side and the diagnosis side in the remote diagnosis system. In this invention, an authentication server communicating with a diagnosis requesting side terminal and a diagnosis side terminal carries out: at the beginning of a remote diagnosis, judging whether or not an image obtained in the diagnosis requesting side terminal satisfies a first condition; at the beginning of the remote diagnosis, judging whether or not an image obtained by the diagnosis side terminal satisfies a second condition; and upon detection that affirmative judgments are obtained in the first and second judgings, generating authentication data including information concerning the diagnosis requesting side terminal, information concerning the diagnosis side terminal and a diagnosis time. Accordingly, it is possible to guarantee that both terminals have an appropriate capability and etc.

The invention discloses a method for extracting features of a crack acoustic emission signal of a drawing part. The method comprises the following steps of: first, preprocessing an acquired original acoustic emission signal in a computer; then, performing empirical mode-based decomposition on the preprocessed acoustic emission signal to obtain n intrinsic mode function components and a residual component; next, performing Hilbert transform on each intrinsic mode function component and expressing the amplitude of the signal as a local wave time-frequency spectrum in Hilbert space; later on, dividing the plane of the local wave time-frequency spectrum into m regions equally, respectively calculating local energy of a time-frequency domain of each region, normalizing the local energy of the time-frequency domain of each region and taking the normalized local energy of the time-frequency domain as an initial feature parameter; and finally, performing a genetic algorithm operation on the initial feature parameter after a plurality of iterations, and obtaining an optimal feature parameter by realizing automatic reorganization and optimization on the initial feature parameter. By using the method, interferences caused by other components are eliminated; the signal-to-noise ratio is improved; the optimal feature parameter can be searched quickly; the diagnostic time can be shortened remarkably; and the diagnostic efficiency can be improved.

A diagnostic apparatus and method are described. The diagnostic apparatus includes a diagnostic model unit configured to diagnose time-series data based on a model structure and parameters of a diagnostic model performing probability model-based analysis. The diagnostic apparatus also includes a learner configured to change the parameters using the time-series data as training data, and a change detector configured to detect a parameter change and output an alarm signal based on the detected parameter change.

The invention belongs to the technical field of medical information, and particularly relates to a medical image multi-stage classification method based on a symmetry theory. The method comprises the following steps that: images to be classified provide classification requests, wherein the images to be classified need to be original medical image data; and an image preprocessing process comprises the steps of image-based modeling, multi-stage classification and result display. The concepts of weak symmetry and high symmetry provided by the invention belong to redefinition on the medical images. A weak symmetry judging algorithm and a high symmetry judging algorithm are provided for realizing the multi-stage classification of the medical images. The classification accuracy of the multi-stage classification is very high, and the direct connection and layer-by-layer deep classification is realized in each stage, so the diagnosis precision of a doctor is improved, and the diagnosis time of the doctor is shortened. The medical image classification is realized by adopting the symmetry theory, so that the medical image multi-stage classification method based on the symmetry theory has higher accuracy.

A computer-implemented method is disclosed for comparing three dimensional (3D) digital medical images. The method uses a reference MPR to position subsequent MPRs in one or more other 3D digital medical images so their content matches the reference MPR. The matched MPRs may then be used by a medical professional to diagnose a patient condition. The ability to quickly and automatically position matching MPRs for multiple 3D images eases the medical staff workload and shortens diagnostics time. Matching MPRs provides an effective way to view the 3D volumes for anatomical changes over time and to monitor medical conditions such as stenosis and tumors.

The invention discloses a deep learning-based intelligent X-ray film diagnosis method. The method comprises the following steps: image data of chest X-ray films stored in a preset memory are acquired;the image data of the chest X-ray films are augmented in a preset mode; the augmented image data are subjected to preprocessing operation to enable data features to meet a standard; the preprocessedimage data are classified to form a plurality of classification models, and model training is designed for each classification model to generate a model training prediction result; a final CNN featuremap is acquired, and according to the CNN feature map, a thermodynamic map is generated; and according to the model training prediction result of each classification model, an integration result is calculated and obtained. Deep learning on pulmonary disease characteristics in a large number of chest X-ray films can be carried out, multiple pulmonary diseases can be accurately predicted, a diagnosis basis is provided, the prediction time is greatly less than manual diagnosis time, and the time of a doctor is saved.

The invention discloses a time series based treatment effect evaluation method. The time series based treatment effect evaluation method comprises the steps of: acquiring lung CT image data, wherein the lung CT image data is composed of a plurality of lung sections; performing pre-processing on lung CT image data; designing a lung nodule detection model according to the pre-processed lung CT imagedata; designing a lung nodule classification model; designing a time axis based lung cancer prediction model according to the lung nodule detection model; and training the lung nodule detection modelaccording to the number of samples per batch. Based on the deep learning, a time series is added, the method dynamically analyzes the changes of the lung nodules, so that a doctor can more accuratelydiagnose a treatment effect of the lung cancer and predict the early recurrence. The diagnosis time of the algorithm is far less than the doctor's manual diagnosis time, and the important steps of the doctor's diagnosis can be assisted in a diagnosis process, which can save a lot of time and energy of the doctor.

The invention provides a vehicle diagnosis method and device. The method comprises steps that a level signal of a selected pin in a vehicle diagnosis system interface is collected to acquire voltage characteristics of the level signal; according to the voltage characteristics, candidate communication protocols associated with the selected pin are determined; a connection request signal based on the candidate communication protocols is sent to the selected pin to determine whether the candidate communication protocols contain a target communication protocol; if yes, communication between an automobile diagnostic device and an electronic control unit ECU of the vehicle is established based on the target communication protocol. The method is advantaged in that the scanning protocol quantity of OBD interface pins is reduced, scanning efficiency is improved, and the vehicle diagnosis time is shortened.

The invention provides an electronic patient record based medical query system and method. The method includes the following steps: the contact area between the finger of a user and a touch screen of a mobile terminal is calculated when the user clicks on an electronic record. When the contact area is larger than or equal to a preset area and the click time is longer than or equal to a preset time, a text content of the electronic patient record can be acquired by the click of the user. Words in the text content are parsed, and then the parsed text content is displayed on a floating window. Key words chosen by the user from the floating window are acquired and sent to a medical cloud platform. The medical data returned from the medical cloud platform according to the key word query is received. By utilizing the method, based on the data of electronic patient records, further query of medical data from the far-end medical cloud platform can be achieved, the diagnosis time for medical treatment of patients is shortened, the medical cost is reduced and the diagnosis efficiency of medical treatment is also improved.

The invention relates to a method of positioning an area under scanning, a medical scanning imaging system and a computer-readable storage medium. The method includes: adjusting a ranger to obtain positional information of positioning start point and endpoint of the area under scanning so as to determine positional information of the area under scanning. Operations are greatly simplified for doctors, and diagnostic time is shortened for patients; since scanning positioning sheets for a patient is not required, the patient takes less radiation dosage.

The invention discloses a method and a system for predictive maintenance based on motor parameters. The method starts from the local operation condition of the motor; two non-electrical acquisition parameters of temperature and vibration of the motor without direct connection are acquired for cross calculation. The real-time working state of the motor is mapped by adopting the matrix eigenvalue vector; the function relationship among the temperature, the vibration and the health degree of the motor can be realized, the linear change relationship between the local and comprehensive health degrees of the motor can be obtained, predictive maintenance is realized by judging the change trend of the health degree within a period of time, the diagnosis time of traditional motor fault diagnosis isadvanced, and irreversible damage to the motor can be well prevented. According to the method, the abnormal working state of the motor can be obtained more accurately, the statistical calculation flow is independent of the real-time calculation flow, the edge calculation pressure of acquisition equipment is reduced, and unnecessary hardware cost is avoided; and predictive maintenance can be positioned to a part of the motor, so that the manual detection cost is reduced.

The invention disclose a quick intelligent diagnosis method for the infant pneumonia on the basis of a hybrid deep learning model, and solves the problem that the body of an infant is injured since existing infant pneumonia diagnosis time is overlong or a misdiagnosis happens. The quick intelligent diagnosis method comprises the following steps of: S1: measuring and collecting each piece of physiological index data of the infant, and according to whether the infant suffers from the pneumonia or not, marking the data; S2: carrying out data cleaning and abnormal data rejection on non-breathing audio data, and constructing a dataset used for training a pneumonia diagnosis model; S3: constructing a dataset used for training a rale identification model; S4: carrying out long short-term memory (LSTM) training; and S5: carrying out deep neural network (DNN) training for interpreting each piece of physiological index data of a patient so as to judge whether the patient suffers from the pneumonia or not. According to the quick intelligent diagnosis method disclosed by the invention, the diagnosis rate and the accuracy of the infant pneumonia can be effectively improved, and serious injuriescaused for the body of the infant due to overlong diagnosis waiting time or the misdiagnosis can be avoided.

The invention discloses an intelligent auscultation system. The system comprises an electronic stethoscope, a patient terminal, a doctor terminal and a server; the server comprises a data acquisition module, a data processing module, an auscultation database, a preliminary judgment module, a data sending module, a data receiving module and a notification module; the preliminary judgment module is used for comparing and analyzing the auscultation data of the patient, the auscultation data of the normal person and various pathological feature information to obtain a preliminary judgment result; the data sending module is used for sending an audio signal detected by a patient and a preliminary judgment result to the doctor terminal; and the data receiving module is used for receiving the diagnosis result sent by the doctor terminal. The system can automatically perform preliminary judgment on the audio signal of the patient of the user to obtain a preliminary judgment result, provide a diagnosis direction for a doctor and reduce the diagnosis difficulty and diagnosis time of the doctor. When the disease degree in the preliminary judgment result or the diagnosis result is serious, the relatives of the patient are timely notified, so that the relatives can timely take response measures.

The invention discloses an epileptic focus positioning data processing method and system and a storage medium. The method includes the steps: acquiring electroencephalogram data and magnetic resonance imaging data of a patient; constructing a head model of the patient according to the magnetic resonance imaging data of the patient; screening the electroencephalogram data of the patient by a sparse Bayesian algorithm; performing tracing and positioning on the head model of the patient according to the screened electroencephalogram data and displaying an epileptic focus on the head model of the patient. The head model of the patient is constructed according to the magnetic resonance imaging data, the electroencephalogram data are screened by the sparse Bayesian algorithm, finally, tracing and positioning are performed on the head model of the patient according to the screened electroencephalogram data, and the epileptic focus is displayed, so that dependency of a focus analysis process on related clinical doctors or professional electroencephalographers is reduced, diagnosis time is shortened, and diagnosis expenses are reduced. The method, the system and the storage medium can be widely applied to the technical field of disease data processing.

An apparatus and a method for diagnosing actuators in a vehicle can improve accuracy and reliability of a failure diagnosis by allowing a diagnostic control apparatus in a vehicle to measure power data and implementing a fast diagnosis by reducing a separation time and a diagnosis time of a measurement apparatus.

The invention discloses a rapid diagnosis and detection test paper card for detecting swine fever as well as a preparation and application method of the rapid diagnosis and detection test paper card. The rapid diagnosis and detection test paper card comprises a PVC base plate, a sample pad, a gold-labelled antibody glass fiber membrane, a nitrocellulose coating membrane, water absorption paper, a detection line and a quality control line. The rapid diagnosis and detection test paper card is applied based on a colloidal gold immunochromatography technology; in a detection process, a swine fever antigen in a sample is combined with a colloidal gold coated antibody to form an antigen-antibody complex, the antigen-antibody complex flows to the other end of an NC membrane along test paper, and when the complex flows to a detection line area on the membrane, a specific antibody fixed on the membrane can capture the complex and gradually coagulate the complex to form a visible detection line, an uncombined colloidal gold antibody flows through the detection line area and is captured by a second antibody of the quality control line to form a visible quality control line, and if the quality control line appears, the occurrence of immunochromatography can be determined, namely, the test paper is valid; and if the detection line appears, a swine fever virus can be determined to exist in the sample. The rapid diagnosis and detection test paper card disclosed by the invention can be used for judging results within 5 minutes, and has the characteristics of short diagnosis time and strong specificity.

The invention provides a novel missile fault prediction device which is widely used in the aerospace automation measurement and control technical field. The device is mainly composed of a PXI measurement and control system, a KVM integral machine, a printer, a test cable, a historic fault database, and missile fault prediction software. By combining virtual apparatus technologies and automatic measurement and control technologies, the device is in direct butt joint with the missile fault prediction software. All previous missile fault diagnosis results are automatically stored in the historic database, and by employing a prediction algorithm, a next possible fault position is predicted, and a part most likely to break down is found, thereby helping the missile fault prediction software to rapidly determine a fault scope, and avoiding a diagnosis mode starting diagnosis from a fixed source. The device can effectively reduce diagnosis time and improve test efficiency, possesses higher use values, and is already applied to the test and fault diagnosis filed of missiles in various tactical models.

The invention discloses a 3G wireless communication-based colostomy complication remote diagnosis system which comprises a plurality of medical centers; all the medical centers are communicated with 3G wireless network base stations by serial ports or networks; the 3G wireless network base stations are respectively connected with a 3G public wireless network; each medical center comprises a diagnosis control terminal which comprises a central processing unit (CPU); the CPUs are connected with the 3G wireless network base stations by routers; each CPU is also connected with a display device, a data storage device and a data acquisition device; and the CPUs are also connected with a computer used for expert consultation diagnosis. The 3G wireless communication-based colostomy complication remote diagnosis system has the advantages of greatly shortening the diagnosis time, reducing the financial burden on a patient, and being convenient for the patient to go to the nearest hospital.

The invention discloses a physique recognition method based on feature selection and a classification model. The physique recognition method comprises the steps: generating answers of all questions ina'traditional Chinese medicine physique classification and judgment table ' through a random number generation algorithm; Calculating a corresponding physique type under the current answer accordingto a judgment method and a judgment standard of the traditional Chinese medicine physique classification and judgment table; adopting a feature selection algorithm to select a representative part of questions from all the questions of the'traditional Chinese medicine constitution classification and judgment table '; Designing a classification model, and performing training by utilizing answers ofpart of questions and corresponding physique types to obtain a physique recognition model; According to the method, technologies such as a random number generation algorithm, a feature selection algorithm and a classification model are introduced, the diagnosis time is effectively shortened, the diagnosis efficiency is improved, the effectiveness of physique recognition is ensured, and certain market value and popularization value are achieved.

The invention makes it an object to provide a memory fault diagnostic device and a memory fault diagnostic method which can diagnose a memory fault at a minimum time using an interim time after the execution of an application in a predetermined control cycle, and can minimize a fault diagnostic time for the whole memories in a diagnostic object area. An area of a memory has a diagnosis area (41) and a non diagnosis area (42), with the diagnosis area divided into a plurality of Row areas which do not overlap each other, and each of the Row areas is divided into a plurality of Cell areas which do not overlap each other. A memory fault diagnostic method has a diagnostic step in a Row area to diagnose between Cell areas with respect to all the combinations of a set of Cell areas in the Row area, and a diagnostic step between Row areas to diagnose between Row areas with respect to all the combinations of a set of Row areas in the diagnosis area. A Row area size is determined to be a size in which a time of the diagnosis in a Row area becomes equal to a time of the diagnosis between Row areas.