45results about How to "Addressing Individual Differences" patented technology

The present invention provides a dynamic model directed angiography three-dimensional reconstruction method, pertaining to an intersectional field of digital image processing and medicine imaging, for satisfying special requests of auxiliary detection and surgery guidance for cardiovascular disease in clinical medicine. The invention includes steps of angiography image preprocessing, vascellum segmentation, vascellum skeleton and radii extraction, model directed vascellum base element recognition, vascellum matching and vascellum three-dimensional reconstruction. The invention also provides a cardiovascular dynamic model construction method including steps of cardiovascular slice data extraction, cordis static and dynamic model building, and static and dynamic model building. According to the invention, good angiography three-dimensional reconstruction result may be obtained, which effectively assists detection and surgery guidance for cardiovascular disease, thereby satisfying clinical requests.

The invention relates to a facial feature based driver attention state detection method. The detection method is characterized by comprising the following steps of performing PERCLOS based driver fatigue state detection; performing blink frequency based driver fatigue state detection; performing sight line distribution based driver attention distraction state detection; and sending out early warning for driver fatigue and attention distraction state, wherein the early warning for fatigue or attention distraction state is carried out continuously for a certain time to avoid frequent switching between different states; the early warning priority is as follows: fatigue driving is greater than attention distraction that is greater than normal driving; and the attention state of the driver can be accurately detected through the physiological reaction characteristics of the driver, and early warning is sent out for driver fatigue and the attention distraction state; collection and extraction of the facial features of the driver are not included; the sampling efficiency of the adopted driver facial feature collection and extracting system is fHz; and the frequency for reading the driver facial feature is f<0> Hz.

The invention relates to a matching method for three-dimensional coronary angiography reconstruction, which belongs to the crossing field of digital image processing and medical imaging, and aims to meet the special requirements on the auxiliary detection and the surgical navigation of angiocardiopathy in clinical medicine. The method comprises two steps, i.e. model guidance contrastographic picture vessel segment matching and contrastographic picture puncta vasculosa matching. The matching method uses a multi-scale vascular tree model so as to better guide vascular matching, simultaneously can well solve the problem of individual difference, and improves the matching precision of vessel segments and puncta vasculosa through the method of iterative matching and largest declination of searching potential energy. The matching method for three-dimensional coronary angiography reconstruction can obtain very good angiography matching results, thereby solving the difficult problems in the automatic three-dimensional reconstruction by the multi-view angiography map.

The invention provides a mechanical device and a method for achieving constant-speed, constant-pressure and non-missing plain canning of a B ultrasound probe. The device comprises a guide rail assembly and a guide rail assembly bracket; the guide rail assembly comprises an X axial guide rail, a Y axial guide rail, a Z axial guide rail, and a rotatable B ultrasound probe clamp which is mounted at the bottom end of the Z axial guide rail; a pressure sensor and an angle measuring sensor are arranged on a lower bracket of the Z axial guide rail; the guide rail assembly is connected with the guide rail bracket through a rotating shaft and can be rotated around the rotating shaft; the guide rail assembly rotates to drive a motor and a rotating speed reduction device; the transmission mode of the guide rail is performed through a precision lead screw or a gear belt; the B ultrasound probe is driven by a driving mechanism to axially move at constant speed in the X, Y and Z directions; meanwhile, the probe is swung under the effect of the probe clamp rotating mechanism; a track assembly is driven by the rotating mechanism to rotate to adjust the vertical relationship between the ultrasound direction and the detection surface, so as to achieve constant-pressure, constant-speed, non-missing and overlapping-free scanning of the B ultrasound probe.

A control technology for a synchronous motor for suppressing rotational pulsation caused by variation in individuals without making a control algorithm complex is provided. In a motor drive system which is a control device for a synchronous motor, in order to suppress the pulsation component of N times as high as the AC frequency for driving the synchronous motor, a controller in which the phase property of the disturbance response of the controller with respect to the pulsation frequency is within ±45° is arranged. Therefore, the torque pulsation component generated from distortion in induction voltage or variation between phases is suppressed.

The invention discloses a three-dimensional fine blood vessel reconstruction method. The three-dimensional fine blood vessel reconstruction method comprises the following steps of 1, loading a three-dimensional fine blood vessel original image; 2, performing blood vessel property-based image preprocessing; 3, performing blood vessel property-based characteristic matching; and 4, performing image filling and boundary processing. The invention furthermore discloses a three-dimensional fine blood vessel reconstruction system. With the adoption of the three-dimensional fine blood vessel reconstruction method and system provided by the invention, a filling region for repairing a missing part is obtained by mapping a position relationship and calculating an overall matching degree, and the missing part is repaired through the filling region to obtain a complete high-precision image, so that the accuracy of three-dimensional fine blood vessel reconstruction is high.

An image scanner is provided with a super-resolution processing function for generating a high-resolution image using multiple pieces of low-resolution image data. The scanner device then measures an initial MTF value without performing the super-resolution processing, refers to an MTF properties table in which is registered a number of pieces of low-resolution image data for a scanner device of that type to achieve a pre-set target MTF, and registers the number of images that corresponds to the initial MTF value in the scanner, if that number is present, and registers an interpolated value if that number is not present. The image scanner then performs the super-resolution processing using the registered number of pieces of low-resolution image data, and outputs image data of a target resolution.

The invention aims at providing an anaesthetic balance control device. The device comprises a data filter module and a drug balance control module. Pre-stored electrocerebral indexes, noxious stimulation data drug usage data are divided into electrocerebral index-sedative data and noxious stimulation data-sedative data. Corresponding drug target control indexes are selected according to electrocerebral index and noxious stimulation data changes in certain period, threshold values are preset, and injection of muscle relaxants are increased or stopped based on the set threshold values. Meanwhile, bispectral index, noxious stimulation data, neuromuscular retarding depth data change tendency slope and the like can be analyzed dynamically, and transverse balance adjusting control is performed on three signals. The invention further provides a work method of the device. According to the device and the method, the problem the imbalanced usage of three drugs is solved, effects of individual differences are prevented, the safety during anesthesia is guaranteed to the largest extent, and good social values and application prospects are provided.

The invention discloses a cross-subject EEG cognitive state recognition method based on a prototype clustering domain adaptation algorithm. According to the method, the concept of category domains isintroduced, on one hand, on the basis of multi-source domain alignment of labels, feature distribution differences between different categories are considered, structural fine-grained alignment underthe category conditions between different source domains in a feature space is researched, and the problem of category imbalance in the multi-source domains is converted into a mode of the category domains; and prototype theoretical clustering alignment between the source domain and the target domain is carried out, i.e., clustering between similar source domains is carried out on the target domain by taking a dynamic adjustment prototype center as a constraint, and similar features and sparse heterogeneous features between the domains are realized, wherein the former realizes intra-domain class conditional structure feature alignment, and the latter realizes global fine-grained structure feature alignment. According to the invention, the method can be compatible with category balance andimbalance, effectively solves the problem of individual difference of electroencephalogram signals in the field of brain cognitive calculation, has high generalization ability, and can be well suitable for clinical diagnosis and practical application.

The invention relates to a brain-computer interface system based on auditory attention and multifocal electrophysiology and an implementation method. The brain-computer interface system based on auditory attention and multifocal electrophysiology is characterized in that the brain-computer interface system comprises an auditory stimulator module, a signal acquisition module, a signal processing module and an execution module; the auditory stimulator module can generate sound sources with multiple frequencies and multiple sound tracks simultaneously, and the sound sources are distributed at different positions of virtual auditory space; all the sound sources are controlled by a same M sequence, and the sound sources produce sounds when the M sequence is 1 and do not produce sounds when theM sequence is 0; the signal acquisition module collects and acquires mixed electrophysiological signals of auditory response through sensors, and signal amplification and simulation/digital conversionare conducted; the signal processing module conducts quick mutual closing calculation on the mixed electrophysiological signals and the M sequence, and auditory response signals corresponding to allthe sound sources are acquired; and then characteristic value extraction is conducted on the auditory response signals of all the sound sources, characteristic vectors are classified through a distinguishing and classifying module, and the sound sources drawing the attention of a tester are decoded, so that the mind of the tester is determined, and the execution module is controlled to execute corresponding action.

The invention discloses a multi-data-source data acquisition method. The method comprises the steps of obtaining to-be-acquired data through a preset protocol; when the to-be-collected data is the data of the identification code device, scanning an identification code on the identification code device through a scanning device to obtain the to-be-collected data; when the to-be-collected data is inthe running state of the industrial equipment, obtaining the to-be-collected data through a sensor; when the to-be-collected data is the questionnaire data, obtaining the to-be-collected data througha mobile terminal; processing the to-be-collected data, and displaying the to-be-collected data through a display screen; and uploading the to-be-collected data to a cloud, so that the cloud sends the to-be-collected data to an upper computer. The invention also discloses a multi-data-source data acquisition device and a multi-data-source data acquisition system. By adopting the embodiment of theinvention, the data of a plurality of data sources can be automatically acquired, and the instantaneity, reliability and safety of equipment communication are ensured.

The invention provides an online gesture recognition method for the myoelectricity individual-difference problem. The method comprises the following steps: (1) establishing a gesture myoelectricity data set to obtain initial data; (2) training an initial classification model; (3) carrying out KKT (Karush-Kuhn-Tucker) discrimination of newly added samples; (4) using DBSCAN density clustering to obtain a preliminary training set; and (5) carrying out secondary screening to obtain a final training set. The online gesture recognition method for the myoelectricity individual-difference problem provided by the invention combines a DBSCAN density clustering algorithm, improves an original KKT-SVM incremental-learning method, and is used for online myoelectricity gesture recognition to solve the individual-difference problem.

The invention relates to a similar case recommendation method based on comprehensive feature representation and an improved width-depth model, and belongs to the technical field of computer natural language processing. According to the invention, the method comprises the steps: firstly, obtaining comprehensive features described by medical records through a comprehensive feature representation model, and performing disease screening; secondly, processing the discrete features by adopting a cross feature method, inputting the discrete features into a linear part, combining comprehensive features described by medical records with shallow model features, and inputting the combined features into a recommendation sorting part taking a gate loop unit as a core; and finally, on the basis of hundreds of candidate cases, outputting dozens of case recommendation items. According to the method, the personalized case recommendation is realized, a recommendation sorting algorithm model combining atraditional shallow linear model and a deep network model is provided, and the accuracy of similar case recommendation is improved.

An oral cavity palate soft and hard tissue segmentation method based on an attention mechanism and integrated registration comprises the steps that firstly, CBCT images are acquired, and after data labeling, the images are divided into a training set, a verification set and a test set; secondly, inputting the training set into a built oral palate soft and hard tissue segmentation network model; in addition, a random augmentation method is added during network training, and random scale and different transformation type enhancement is carried out on input data; predicting a tissue segmentation result, and quantitatively evaluating a prediction effect of the model on a test set; and finally, performing integrated registration according to a multi-palate soft and hard tissue segmentation result. The oral cavity palate soft and hard tissue segmentation and registration method fills the blank of oral cavity palate soft and hard tissue segmentation and registration, solves the problem that tissue segmentation is not accurate enough, shortens the time for searching implantation sites of different cases, and provides technical support for case analysis and design of orthodontic implantation nails.

The invention provides an artificial intelligence processing method supporting online learning and a processor. By adopting a double-judgment mode combining adaptive template matching, adaptive high/low threshold judgment and neural network judgment, the system has an online learning capability, can adapt to time sequence signal characteristics of using equipment and users, solves the problem thatthe common time sequence signal characteristics have individual differences, and effectively improves the automatic classification accuracy. Compared with a traditional neural network back propagation algorithm for achieving hardware implementation of online learning, the method is small in parameter quantity and operand, only a small amount of data is stored, and compared with online learning ofthe neural network, the power consumption and the needed storage quantity are greatly reduced. All parameters in the online learning hardware module can be freely configured according to different application requirements, so that an online learning algorithm can be flexibly deployed on a processor, and the online learning hardware module is suitable for application scenes of automatic classification of various time sequence signals.

The invention relates to the field of biological products, in particular to a cell collection shaking table and a cell collection method. The invention provides the cell collection shaking table, which comprises a box body, a driving device and a tray assembly, wherein a closed collection area is arranged in the box body, the tray assembly is arranged in the collection area, a cell factory is arranged on the tray assembly, the driving device drives the tray assembly to rotate circumferentially with a set radius; the invention also provides a cell collection method adopting the cell collectionshaking table, wherein after the cells are cultured in the cell factory, the cell factory is fixed on the tray assembly, the tray assembly rotates for a set time with a set radius and a set rotating speed, and the set rotating speed is 100-300r/min. The cell collection shaking table and the cell collection method are suitable for cell collection in a cell factory and have simple structure, simpleand convenient operation, high collection efficiency and good collection effect.

The invention discloses an electrocardio vector reconstruction method based on unsupervised learning. The method carries out the reconstruction of an electrocardio vector of an inputted standard 12-lead electrocardiogram through employing a neural network. In the training process, a method of firstly mapping the standard 12 lead to the electrocardiogram vector and then restoring the 12 lead electrocardiogram by using a projection method is used, so that the problem that the traditional method depends on the corresponding data of the 12 lead and the electrocardiogram vector is solved, the utilization efficiency of the data is obviously improved, and the data cost is reduced. During reconstruction, a neural network is used for recalculating a projection vector to perform reconstruction froman electrocardiogram vector to a 12-lead electrocardiogram, and a regularization term is used for constraining the projection vector in a final loss calculation module, so that the interpretability and the accuracy of a reconstruction process are ensured while the individual difference of the electrocardiogram is solved. The multi-order differential loss is used in a final loss calculation module,so that the problems of low frequency, such as baseline interference and the like, are avoided on the basis of ensuring morphological characteristics.