227results about How to "Easy to describe" patented technology

A system to measure fluid flow characteristics in a pipeline, meter, and methods includes a pipeline having a passageway to transport flowing fluid therethrough, a process density meter including at least portions thereof positioned within the pipeline to provide flowing fluid characteristics including volumetric flow rate, fluid density, and mass flow rate of the flowing fluid, and a fluid characteristic display to display the fluid characteristics. The process density meter includes a vortex-shedding body positioned within the pipeline to form vortices and a vortex meter having a vortex frequency sensor to measure the frequency of the vortices and to determine the volumetric flow rate. The process density meter further includes a differential pressure meter positioned adjacent the vortex-shedding body to produce a differential pressure meter flow rate signal indicative of the density of fluid when flowing through the pipeline. The process density meter also includes a thermal flow meter positioned adjacent the vortex-shedding body to produce a mass flow rate signal indicative of the mass flow rate of fluid when flowing through the pipeline. The process density meter produces an output of a volumetric flow rate, a flowing fluid density, and a mass flow rate to be displayed by the fluid characteristic display.

The prostate gland or other region of interest is stimulated with laser light, resulting in ultrasound waves (photoacoustic effect) which are focused by an acoustic lens and captured by a specific 1- or 2D sensor array and subsequently displayed as a C-scan on a computer screen. The amplitude of the ultrasound waves generated by laser stimulation is proportional to the optical absorption of the tissue element at that spatial location. Variability in tissue absorption results in C-scan image contrast. The photoacoustic imaging is combined with an ultrasound C-scan image produced with a plane ultrasound wave applied to the region of interest.

The invention relates to a convolutional neural network-based user attribute inference method and apparatus. The method comprises the steps of establishing a self-centered network according to attributes of user nodes and a friend relationship; and extracting attribute information of the user nodes in the self-centered network and hidden information comprised in the friend relationship by adoptinga convolutional neural network, and inferring missing attributes of a user by utilizing the hidden information. For a social network which cannot directly obtain the friend relationship or has relatively high obtaining difficulty, the missing attributes are subjected to classification prediction by only utilizing the attribute information of the user through adopting the neural network. The limitation of manual definition of a similarity function can be well avoided; and relationships among different attributes and different attribute dimensions can be better showed through convolution operation of a convolution kernel, so that the missing attributes of the user can be inferred efficiently and accurately.

The invention relates to a binocular initiative vision monitoring device for precision machining. The feature is that it fixes three supports on base, and sliding rails are installed in the three supports and plumbing to bottom plane, sliding blocks are installed in the sliding rail, the round guiding rail connecting to the three sliding blocks, two dollies installed on the round guiding rail, each dollies having a cradle head that is installed a video camera. The base line and location between the two video cameras would be adjusted by sliding on the guiding rail. The vision system could flexibly adjust the observing anger. It has the advantages of parallel structure, simple moving mode and simple vision computing mode.

The invention aims at providing a hyperspectral image classification method based on spatial and spectral features and sparse representation. The method comprises the following steps that: hyperspectral high-dimension data is read, dimension conversion is carried out, and the hyperspectral high-dimension data is subjected to normalization processing, wherein the contained sample class number is L; the image spatial vein features and the spectral features are respectively extracted to obtain the spatial vein features T1 and the spectral features T2; a spatial and spectral feature set T of images is obtained through merging the spatial vein features and the spectral features, wherein T={T1, T2}; a part of samples is respectively selected from T for L classes to form a training set A, and a test set is set to a state that the set of all of the L classes in the T is M; the M and A are utilized for solving a sparse representation coefficient S<^> of the hyperspectral data for image reconstruction, and in addition, corresponding redundancy in each class is calculated; and the classes of the samples are determined according to the redundancy. The hyperspectral image classification method has the advantages that the information in hyperspectral images can be sufficiently utilized, and the hyperspectral images can be perfectly depicted through the spatial and spectral features; the classification precision can be improved; and the method can be applicable to different hyperspectral images, and the applicability is high.

The invention discloses a lithium ion storage battery temperature combinational circuit model including an energy balancing circuit and a voltage responsive circuit. The energy balancing circuit predicts the continuous operation time and charged state SOC of a battery, and the voltage responsive circuit stimulates the transient response of the battery, and elements of the circuit are variables and change continuously according to the change of the environment temperature and SOC. The parameter identification method of the lithium ion storage battery temperature combinational circuit model includes four steps of charged state and battery open-circuit voltage relation identification, battery capability and temperature relation identification, self-discharge resistance identification, and internal resistance and polarized parameter identification. The lithium ion storage battery temperature combinational circuit model and the parameter identification method thereof make the stimulation approximate to the real response of the storage battery at different temperatures and give guidance to battery state estimation, and have the advantages of distinct characteristic, visual construction, clear physical meaning, convenient parameter identification, and easy application in engineering.

The invention discloses a QR Code image super-resolution reconstruction method based on sparse representation, which solves the problem of super-resolution reconstruction of low-resolution QR Code two-dimensional code images. Using the edge gradient feature and texture feature of the QR Code two-dimensional code, the operator extracts the image feature to obtain the feature block; through the dictionary learning algorithm, obtain the learning dictionary of the low-resolution image block and the corresponding high-resolution image block; use sparse representation Theoretically, sparsely encode the input low-resolution image feature blocks, and combine the learning dictionary to obtain the high-resolution image blocks corresponding to the input low-resolution image blocks; apply global constraints to synthesize all high-resolution image blocks into the final high-resolution QR Code two-dimensional code image to achieve super-resolution reconstruction. The invention utilizes image characteristics to effectively reconstruct a high-resolution QR Code two-dimensional code image with clear edges and retains a large number of high-frequency details, and is suitable for super-resolution reconstruction of the QR Code two-dimensional code image.

The present invention relates to a microwave radiation antenna capable of making percutaneous puncture to cure tumor of several regions of human body. Said invention is formed from antenna connector, hard coaxial line, ceramic tube, water inlet tube, water outlet tube, temperature-measuring system, stainless steel sleeve and handle. Its output power can be up to above 100w, and is therapeutic effect is reliable and safe.

The invention discloses a method for reconstructing a compressively sensed spectral image based on structural clustering sparse representation, which method solves the difficulty of the existing method for reconstructing the spectral image that a partial structure of the spectral image is hard to be recovered accurately since spatial and spectral correlations are not fully used. The method comprises implementation steps as follows: 1, performing back projection upon input encoded sensing data of an spectral image to obtain an initially reconstructed spectral image; 2, segmenting the reconstructed spectral image to obtain a series of overlapped three-dimensional spectral image blocks; 3, de-noising the three-dimensional spectral image blocks by using a sparse representation method based on structural clustering; 4, recovering the whole spectral image by using the spectral image blocks subjected to de-noising procession; 5, updating the spectral image by using a back projection technology, iterating the steps 2-5 so as to obtain a final reconstruction result. Experiment results show that the method disclosed by the invention can reconstruct a relatively fine spectral image structure, and the reconstructed spectral image has a relatively high signal to noise ratio.

The invention discloses a rapid and high-precision forward modeling method for a gravitational field of an arbitrary density distribution complex geological body. The rapid and high-precision forward modeling method achieves the unification of efficiency and precision of gravitational field forward modeling through the steps of complex geological body model representation and prism body combined model gravitational field calculation (including weighted coefficient calculation, two-dimensional discrete convolution calculation and gravitational field value synthesis). The rapid and high-precision forward modeling method solves the problems that the existing gravitational field forward modeling method cannot guarantee calculation efficiency and calculation precision simultaneously, and cannot meet the requirements of large-scale gravitational field three-dimensional density inversion and man-machine interaction modeling and interpretation.

The invention provides an anti-jamming model in unmanned aerial vehicle communication and a Stackelberg game subgradient algorithm. The model is characterized in that in a process of unmanned aerial vehicle group communication, a jammer jams user communication and mutual jamming also exists in the unmanned aerial vehicle group; the jammer and the unmanned aerial vehicle group achieve the maximum utility by adjusting respective transmission power. The algorithm is characterized in that firstly a Stackelberg game model is built, wherein the game leader is a jammer, and game followers are unmanned aerial vehicle users; unmanned aerial vehicle users perform communication; the intelligent jammer adjusts own transmission power to the optimum according to the transmission power of the unmanned aerial vehicle users, and then the unmanned aerial vehicle users adjust respective transmission power to the optimum according to the transmission power of the intelligent jammer; power adjustment is conducted circularly and alternately until the unmanned aerial vehicle users and the intelligent jammer converge to the optimal transmission power. The model is complete and the physical significance isclear; anti-jamming scenes in unmanned aerial vehicle communication can be better described.

The invention discloses a method and system for detecting apnea events based on BCG signals, which are used to accurately detect apnea events during sleep. This method locates the potential occurrence interval of respiratory events by identifying the arousal segment, and then divides the event potential occurrence interval into apnea segment, respiratory effort segment, and arousal segment, and then extracts fine-grained features that can describe the breathing pattern from the three segments. , and finally with the help of machine learning methods, it is judged whether the potential occurrence stage of the event contains an apnea event. The system mainly includes: a signal acquisition module, a data processing module, and a detection result output module. The method and system can automatically and accurately locate the potential occurrence interval of apnea events and automatically divide the interval into three different stages, which facilitates the description of breathing patterns from multiple aspects and in a fine-grained manner, and greatly improves the detection accuracy of apnea events Rate.

The invention discloses a classifier integration method based on floating classification threshold, which is characterized by obtaining T optimal weak classifiers are by means of training after T iterations and then combining the T optimal weak classifiers to obtain an optimal combined classifier. In case of aiming at a bi-classification problem, training the T optimal weak classifiers comprises the steps of: (3.1) training the weak classifiers based on a training sample set S with weight omega<t>, wherein t is equal to 1,..., T; (3.2) based on the result of the step (3.1), adjusting sample weights omega<t+1>=omega<t>exp(-yiht(xi))/Zt; (3.3) judging whether t is smaller than T, if so, enabling t to be equal to t + 1 and returning to the step (3.1) until t is equal to T; in case of aiming at multi-classification problem, training the T optimal weak classifiers comprises the steps of: (3.1) training the weak classifiers based on the training sample set S with weight omega<t>, wherein t is equal to 1,..., T; (3.2) based on the result of the step (3.1), adjusting sample weights shown in the description; (3.3) judging whether t is smaller than T, if so, enabling t to be equal to t + 1 and returning to the step (3.1) until t is equal to T. Compared with the prior art, the classifier integration method of the invention can overcome the defect that fixed classification threshold-based classifiers have unstable classification at points adjacent to classification boundary.