288results about How to "Robust" patented technology

Systems and methods are provided that empowers various parties to combine transactional data and local non-transactional data using the collective intelligence gathered from a variety of sources to help the parties make more intelligent decisions relating to consumers. For example, the system can help select consumers based on the probability that the consumers will take advantage of an offer, coupon, or other item. In some embodiments, the present invention can be deployed as a part of a system that processes transactions. In this system, information associated with the transactions is analyzed in conjunction with non-transactional data in order to probabilistically determine whether a further action should be taken with the consumer.

Indirect airflow measurement through breath-operated device is accomplished by incorporating an airflow sensor into the inhaler device along a low resistance channel disposed away from the exhaust chamber of the device and having an input port in airflow communication with a low resistance channel and output input port and low resistance channel are formed in the main housing body of the device, and further incorporating an output port formed near the exhaust changer near the mouthpiece assembly, the output port also in airflow communication with the low resistance channel. A method of measuring airflow in an inhalation device is also described that measures air flowing through the low resistance channel. Another aspect of the invention provides a method that allows for the closure of the devices' airflow ports, by allowing for the rotation of the mouthpiece assembly from open to closed positions relative to the inhaling device's main housing body and towards handle assembly.

The invention discloses a method for improving a BP (back propagation) neutral network and based on a genetic algorithm. The method includes coding the BP network to determine structure of the neutral network, wherein the structure includes the number of hidden layers and the number of units of each layer; adopting real-number coding to code by taking weight and threshold as genes, wherein each neutral network corresponds to a chromosome after coding; using the genetic algorithm to perform selection optimization on the network, wherein selection optimization includes the steps of selection, crossing and variation; training the BP network to acquire a final result; decoding an optimal individual selected by the genetic algorithm to generate a new neutral network, and training the new network by applying a BP training algorithm to acquire a final result. The method combines the genetic algorithm with the BP network, thereby being capable of fully utilizing advantages of the both, the problem that initial weight and threshold of the BP network are difficult to determine can be solved, searching range can be narrowed, training speed of the BP network can be increased, and the problem of local minimum can be improved.

The invention relates to a method for identifying abnormal microblog users. The method comprises the steps of obtaining a plurality of users' microblog data, storing the microblog data into a database, taking statistical distribution of time intervals of user behaviors as behavior time characteristics of the users according to the microblog data of the users, generating behavior time characteristic vectors and defined parameters, calculating Kullback-Leibler divergence between the behavior time characteristic vectors of the normal users and the behavior time characteristic vectors of the users to be detected, judging the users to be detected with the calculated Kullback-Leibler divergence exceeding the defined parameters as the abnormal users, and extracting and showing keywords of contents of the abnormal users. The invention further provides a system for identifying the abnormal microblog users corresponding to the method. According to the method and system, the keywords of the blog article contents of the abnormal users can be extracted quickly, promulgators of junk information such as marketing and advertisements can be identified accurately, and the method and the system are applicable to detection of multiple microblog service platforms, and has the advantages of high accuracy and efficiency and wide applicability.

The invention discloses a partition-based 3D printing filling path generation method. The method includes the following steps: determining an SLC file for entity processing, and a filing path spacing; generating contour offset paths, and saving the innermost contour offset path; determining a scan line angle; carrying out primary partition on a processing area according to the number of the intersection points of the scan line and the offset polygon of the innermost offset contour; carrying out second partition on the primary partitioned result according to the number of intersection points to obtain subarea sets; generating subarea paths according to the intersection points of the scan line and the offset polygon of the innermost offset contour and a relationship among the subareas; adjusting the subarea paths; and connecting remaining subpaths by using a spline curve to obtain the final inner filling path. The partition-based 3D printing filling path generation method has the advantages of strong robustness, adaptation to models with various shapes, easy realization, good versatility, easy embedding into 3D printing equipment, and commercialization realization.

The invention discloses an automatic evaluation method of human body action and a dance scoring system. The method comprises the following steps: on the basis of depth data, selecting human body bone nodes and human body outline nodes; on the basis of the human body bone nodes, constructing human body limb vectors, calculating a space included angle between each human body limb vector and a corresponding template limb vector, weighting and normalizing the space included angles, and calculating an accumulation error which is taken as a diversity degree based on the human body bone node analysis; on the basis of the human body outline nodes, constructing human body outline vectors, calculating a space included angle between two adjacent human body outline vectors, constructing an energy function by a diversity value between the space included angle and space included angle of template outlet vectors, solving a minimum value of the energy function by a graph cut method, and taking the minimum value as the diversity degree based on human body outline node analysis; and carrying out weighting and summation on the diversity degree based on the human body bone node analysis and the diversity degree based on the human body outline node analysis, and taking the obtained value to serve as an evaluation parameter used for balancing a matching degree of the human body action and template action so as to realize the automatic evaluation of the human body action.

The invention discloses a target tracking method and device. The method includes the first step that in an initialized frame, a special mapping link is designed to convert an image into multi-vector mixing features; the second step that extracted multi-vector mixing features are adopted to train a multi-vector correlated filter; the third step that aiming at subsequent sequence images, the trained multi-vector correlated filter is adopted to generate confidence maps of all search locations; the fourth step that the peak value of the confidence maps is selected as a target position, multi-vector mixing features are extracted from the target position through the method the same as the method in the first step, and on-line updating is performed on the correlated filter. By the adoption of the target tracking method and device, a target without prior information can be tracked, high robustness can be achieved on illumination, rotation, shielding and other challenging factors, and quick tracking can be achieved.

The invention provides a UUV (Unmanned Underwater Vehicle) path tracking method based on self-adaption sliding-mode control. The method comprises the steps of 1, initializing; 2, obtaining a current state of a UUV; 3, establishing an underactuated UUV horizontal error equation to obtain the positional deviation xe, ye and a course deviation value psi e; 4, utilizing a sliding-mode control method to respectively design a speed sliding-mode control law, a position sliding-mode control law and a stem phase angle sliding-mode control law and enabling th ud to be zero, the xe to be zero and the psi e to be zero by controlling thrust Xprop, expected speed (shown in the following image) and torque Nprop; 5, updating a self-adaption law of handover gain and a self-adaption law of boundary layer thickness; 6, performing control input saturation compensation; 7, enabling k = k +1, returning to the step 2 and updating the control laws and the self-adaption laws for the next time to achieve the accurate control on the UUV horizontal path tracking. The UUV path tracking method can only depend on a controller which is designed according to a horizontal dynamical model to stabilize the system and is suitable for various underactuated UUVs.

The invention discloses a distribution network fault location method utilizing a natural frequency and an artificial neural network. In the method, fault location is performed by utilizing a principle that main natural frequencies of a line of a fault traveling wave are different when the line fails at different points; and simultaneously data processing is performed by utilizing a hierarchical distributed artificial neural network. After the line causes a single-phase ground fault, the sampling frequency is 1 MHz; a discrete zero-sequence transient signal with the sampling length of 2048 after the fault is selected; fast Fourier transform (FFT) is performed, wherein the transform result is a matrix of 2048*2; and an absolute value of the matrix is solved. According to the model adopted by the method, the fault location is performed by selecting 8 frequency values with large amplitude values as the sample attribute of the neural network, selecting a suitable transfer function and a learning rule, and setting appropriate neural network parameters to construct a back propagation (BP) network model. A large number of simulation results show that the method has a good effect.

The invention relates to a fault diagnosis method of a nonlinear network control system. The fault diagnosis method comprises the following steps of setting a discrete model according to the status quo of the nonlinear network control system having state delay and model uncertainty; obtaining a robust fault detection filter of the nonlinear network control system having data packet dropout based on a method of a fault observer; setting an augmentation vector, generating a residual error dynamic system, and giving out conditions required by mean-square stability of the residual error dynamic system by means of the Lyapunov stability theory; solving for a gain matrix L and a residual error weight matrix R of the robust fault detection filter and judging whether the nonlinear network control system has faults. By means of the robust fault detection filter, fault sensitivity to the nonlinear network control system is improved greatly, strong robust on external disturbance and data packet dropout is achieved, and related fault diagnosis of the nonlinear network control system with the uncertain model can be achieved well.

The invention relates to a self-adaption controller and a method. Concerning about a plurality of uncertain factors of a controlling system in outside world, the invention adopts a self-adaption robustness controlling strategy with high performance. The self-adaption controller comprises that: the output end of a feedforward controller is respectively connected with a position feedback controller, an interference suppressor and a controlled object; one end of the feedforward friction compensation is connected with an input end and the other end is connected with the position feedback controller, the interference suppressor and the controlled object. The self-adaption controlling method comprises that: the self-adaption robustness interference suppressor can online detect and estimate the interference of the system and provide real time compensation; by using a zero phase error tracing method, the feedforward controlling design can realize fine tracing characteristics within limited frequency range. The feedforward friction compensation provides a nonlinear real-time regulation of the friction of the system and improves the whole dynamic performance of the system. The adoption of the self-adaption robustness controller with high performance guarantees the fine dynamic performance of the moving controlled object and fine suppressing ability of outward interference.

The invention discloses a method for actively inhibiting the flexible vibration of a sun sailboard. According to the method, a stepping motor under sine and cosine subdivision drive is an executing mechanism, a satellite system dynamics model is established, after linear simplification is performed on the system dynamics model, the vibration frequency and the damping ratio of a system are calculated, an input former is designed to reshape an original sailboard rotating speed instruction, so that the system finishes sailboard rotating speed continuous drive and effectively inhibits the flexible vibration of the sailboard at the same time, and therefore influences on the load imaging quality by the flexible vibration of the sailboard are reduced. In addition, model parameter variation and design parameters do not have the strong robustness, compared with other sun sailboard flexible vibration control methods adopting piezoceramic materials, dampers and the like, adding of extra hardware devices is not needed, and engineering realization is simple.

The invention provides a fuzzy domain characteristics based optical fiber vibration signal identifying method and mainly aims to solve the problem that existing identifying methods are low in identifying efficiency under the conditions of low sampling rate and similar intrusion events. The fuzzy domain characteristics based optical fiber vibration signal identifying method includes steps of (1) subjecting optical vibration signals to wavelet denoising; (2) subjecting the denoised signals to de-meaning and energy normalization; (3) calculating the normalized signal fuzzy function and sectioning the fuzzy function to be fuzzy domain characteristics; (4) reducing dimensions of sections to construct a signal characteristic set; (5) partitioning the signal characteristic set into a training set and a test set; (6) training an SVM (support vector machine) classifier by the training set; (7) classifying the test set by the trained SVM classifier. The fuzzy domain characteristics based optical fiber vibration signal identifying method effectively extracts the fuzzy domain characteristics of optical fiber vibration signals, has the advantages of high identification rate and wide applicability as compared with the prior art, and can be used for a signal processing subsystem of an optical fiber perimeter security system.

A piezoelectric element includes a substrate, a lower electrode formed above the substrate, a piezoelectric layer formed above the lower electrode, an upper electrode formed above the piezoelectric layer, a protection layer formed on the lateral sides of the piezoelectric layer, and a self-organized monomolecular film formed on the side of each of the protection layer not facing the piezoelectric layer.

The invention relates to an audio scene recognition method and device based on long-term and short time feature extraction. The method comprises: preprocessing an inputted to-be-recognized audio signal; d carrying out short-time audio feature extraction on the pre-processed to-be-recognized audio signal and carrying out long-term audio feature extraction; and carrying out long-term and short-timeaudio feature combination of the to-be-recognized audio signal, inputting the features into a classification model and a fusion model, carrying out classification and identification, and outputting anidentification label of an audio scene. According to the invention, the long-term features of the audio scene are combined based on the conventional short-time feature extraction and complex audio scene information can be represented; the features are inputted into the classification model and the fusion model to carry out classification and identification; and the identification label of the audio scene is outputted. The method and device have advantages of high robustness and good distinguishing performance; the overall characteristics of the scene data can be represented to the great extent; and the recognition efficiency and the stability are high.

The invention discloses a wavelet transform and Arnold based adaptive gray-scale watermark embedded method which comprises the following steps: carrying out square matrix conversion and scrambling processing on an original watermark image; carrying out wavelet transform (three times) on an original host audio to obtain wavelet low frequency coefficients as the embedded pre-selected position of a watermark; automatically searching an optimal embedded sequence from the low frequency coefficients, wherein the optimal embedded sequence is the optimal embedded position of the watermark; and carrying out convolution on a digital watermark image sequence and the optimal embedded sequence, namely, embedding the digital watermark image into the low frequency coefficients of audios. The method is characterized in that in the process of digital watermark extraction, the audios containing watermarks are detected according to the original audio, and a watermark image sequence is extracted from the audios; and in the process of digital watermark reduction, the length and width of the original image are taken as extraction keys, and the extracted watermarks are reduced through the utilization of reverse Arnold conversion and image reconstruction. The method disclosed by the invention is strong in function, quick in computing speed, easy and convenient to implement, and has certain practical values in the aspects of digital image copyright protection and information hiding.

The invention provides a three-dimensional face living body recognition method and device and relates to the technical field of face recognition and image processing. The method comprises the following steps: collecting a three-dimensional face image; carrying out normalization processing on the three-dimensional face image; calculating depth histogram information or related coefficients of the three-dimensional face image according to the depth information of various points of the normalized three-dimensional face image; and judging whether the three-dimensional face image is from a living body or not according to the depth histogram information or the related coefficients. Living body recognition is carried out according to the depth histogram information or the related coefficients, so that the recognition result is high in accuracy; and the three-dimensional face living body recognition method and device have robustness and stability.

The invention discloses a depth and breadth neural network combined speech enhancement algorithm of a digital hearing aid belonging to the speech signal processing technical field. The method comprises following steps: firstly, carrying out speech activity detection to noise contained speech signals; extracting features such as autocorrelation function maximum values and variances of frequency bands of the noise contained speech signals; building a two-value decision device by using a BP neural network; judging speech segments and noise segments; secondly, extracting MFCC and first level MFCC features; detecting noise types by a depth neural network, wherein the depth neural network is formed by cascading a learning vector quantization neural network and the BP neural network; finally, building a breadth neural network formed by connecting various networks in parallel; automatically selecting corresponding neural networks by the breadth neural network according to the noise types; removing the noises, thus obtaining the enhanced speech and improving the speech intelligibility of the hearing aid output speech. According to the algorithm, the training processes of the neural networks are finished offline; the complexity of the test algorithm of the trained networks is low; and therefore, the real timeliness is satisfied.

The invention relates to an adaptive amplifying digital baseband pre-distortion method. The method comprises the following detailed steps of: step A, judging whether an input signal and an output signal of a pre-distortion system are equal or not, if so, disconnecting with a pre-distortion training module, otherwise, performing step B; and step B, using a training method of a memory multinomial coefficient of Kalman filter to set the to-be-estimated memory multinomial coefficient of the pre-distortion training module to be a state variable, establishing a state space model, and using an estimation value of the prior time and a sampling value of the present time to update the state variable to acquire the estimation value of the present time. Compared with the traditional pre-distortion scheme, the technical scheme provided by the invention has the advantages of high robustness and convergence rate; and the requirement of updating parameters in real time can be met through the technical scheme.

The invention discloses a structured light measuring system calibration method based on sinusoidal grating. The method includes producing coding grating, setting up measuring system to capture calibrating board flat images, projecting coding grating, collecting, storing, confirming phase-unfolding initial points, solving wrapped phase and unfolding, restoring the calibrating board flat images of projector imaging planes, calibrating projectors according to the multi-group projector calibrating board flat images at different positions, calibrating cameras according to multiple camera calibrating board flat images at different positions, and acquiring rotation matrixes from projector coordinate systems to camera coordinate system according to calibrating results of the projectors and the cameras. The phase-unfolding initial point can be effectively confirmed by utilizing coding disc images, and image quality of image region is guaranteed to be high; and the designed phase-unfolding method has a noise-proof function, 'line-pulling' conditions in conventional phase-unfolding methods are overcome, and the designed structured light measuring system calibration method has the advantages of simple operation, strong robustness, high measurement accuracy and the like.

The invention discloses a fault diagnosis and safety pre-warning oriented elevator monitoring system and method. A cloud-side processing module is used for receiving data of all machine room board modules and camera modules in multiple elevator cars and achieving the computation and storage functions. The machine room board modules control data of the multiple elevator cars, achieve the computation and temporary storage functions and transmit computation results to the cloud-side module. Elevator car ceiling board modules collect data of the corresponding single elevator cars, transmit the data to elevator car ceilings in an I2C mode to be processed and transmit processing results to the machine room board modules. Elevator car door board modules are used for collecting data of barometer sensors and three-axis acceleration sensors mounted nearby elevator car doors, conducts filtering, integration and vibration analysis on the data and transmit processing results to the corresponding elevator car ceiling board modules for being processed. The fault diagnosis and safety pre-warning oriented elevator monitoring system achieves the real-time monitoring analysis effect through layer-by-layer grading and function decoupling of all the modules and meanwhile improves the elevator running state monitoring and fault pre-warning accuracy.

Disclosed is a monitoring method of a non-invasive household load switching event in real time. The monitoring method of the non-invasive household load switching event in real time comprises the control of the parameters initialization, the calculation of the active power value, the judgment of the steady-state of the active power and the judgment of the load switching event. The monitoring method of the non-invasive household load switching event in real time, without the intrusion into the load inside, has the technical characteristics of high monitoring precision of a bound of the steady and transient state of the load event, strong fluctuation robustness of the active power, a small amount of calculation, a flexible configuration. The monitoring method of the non-invasive household load switching event in real time can provide a reliable basis for all kinds of the non-invasive load identification technologies and the classification measurement technologies.

The invention discloses a video anomaly detection method based on weighted tensor subspace background modeling, which is mainly used for solving the problem that the prior art can not filter out outliers in an image due to the ignorance of space structure information of the image. The implementation process is as follows: firstly regarding training data and observation data as two-order tensors, adopting the tensor analysis method to calculate a projection matrix on each mode, and constituting a tensor subspace; then carrying out robustness analysis on the observation data, weighting each element in the observation data, updating the tensor subspace, projecting the weighted observation data onto the subspace, and reconstructing a background image; and finally carrying out similarity measurement on a reference background and the reconstructed background, and detecting whether an anomalous event happens in a video scene or not. Compared with the prior art, the method can keep the space structure information of the image, filter out the outliers in the image and have robustness. The method can be used for anomalous event detection under the conditions of fixed scenes and slowly-changing illumination in the fields of security protection and monitoring.

The invention discloses an anti-attack and defense method based on an LSTM (Long Short Term Memory) detector. The method comprises the following steps that: 1) generating a candidate detector of an LSTM-FNN (Fuzzy Neural Network) structure, and asking the detector to detect abnormal samples as more as possible on a premise that the detector does not misjudge a normal sample; 2) storing the candidate detector into a register queue, detecting a training dataset by an abnormality detector taken out of the register queue, and deleting the detected abnormal sample to enable different abnormality detectors to cover different abnormal areas; and 3) in a detection stage, detecting a detected sample by all abnormality detectors, and comprehensively judging whether the sample is abnormal or not. Byuse of the method, a neural network is combined with a negative selection algorithm, each LSTM detector guarantees that the normal sample can not be misjudged, the abnormality detector set guaranteesthat the abnormal situation may be covered, and an algorithm detection effect is improved.

The invention discloses a variable structure adaptive trajectory tracking control method of a mobile robot. The method includes the steps of obtaining the actual linear speed and angular speed of a robot according to angular speed of left and right wheels of the mobile robot obtained by a photoelectric encoder, and calculating a deviation; calculating to obtain a deviation between a reference pose and an actual pose according to infrared rays and the pose of the wheeled mobile robot obtained by a gyroscope; and establishing a nonholonomic constraint mobile robot model, converting the model into an angular speed-related second-order subsystem and a linear speed-related third-order subsystem, further obtaining the controlled quantity of the mobile robot, and obtaining input torque of the mobile robot through the controlled quantity. The variable structure adaptive trajectory tracking control method of the mobile robot can realize trajectory tracking control of the mobile robot under the circumstance of unknown parameters and external disturbance, can enable a pose tracking error of the mobile robot to converge to arbitrarily small neighborhood containing the origin, has a good tracking effect, and has relatively high robustness.

The invention relates to the field of computer vision and particularly relates to an improved MSER image matching algorithm. A speeded up robust feature (SURF) and a maximally stable extremal region feature (MSER) are combined to carry out image feature extraction and matching so as to generate a feature vector, and then the Euclidean distance is used to carry out the coarse matching of an image so as to preliminarily correct the space geometric distortion of the image. Then the scale invariance of an H-L feature is applied, and a feature point comprising a large amount of image structure information can be detected. According to the algorithm, the complementarity of feature extraction of two parties in the multiple transformation conditions of the image can be fully utilized, and the robustness of matching between images in a complex environment in a time condition acceptable range is achieved.

The invention discloses a position sensor-free vector control device for a built-in permanent magnetic synchronous motor and belongs to the field of motor control. The position sensor-free vector control device aims to solve the problems that overlarge position observation error is caused during load disturbance, the system is out of control when a position observed value is restrained to an S-pole direction and the position sensor-free low-speed running control performance of the built-in permanent magnetic synchronous motor cannot be improved effectively in the prior art. The position sensor-free vector control device comprises a rotating speed controller unit, a first current controller unit, a second current controller unit, a high-frequency voltage signal generation unit, a straight shaft voltage superposition unit, a coordinate spinning inverse transformation unit, a power converter unit, a low-pass filter unit, a three phase to two phase coordinate transformation unit, a coordinate spinning transformation unit, a high-frequency current signal processing unit, a rotor position observer unit, a rotating speed calculation unit and a current detection sensor. The control devicecan avoid a phenomenon that the system is out-of-control when the position observed value is restrained to the S-pole direction.

The invention generally relates to zwitterionic polymers (including zwitterionic copolymers), such as polymethacrylic structures, with pendent functional moieties, such as therapeutic or biologic moieties. More particularly, the invention relates to phosphorylcholine-substituted methacrylic polymers prepared by free radical polymerization and click chemistry, for example, and compositions and products comprising same, as well as related methods and uses of the compositions, for example, as biological or therapeutic agents and in drug delivery thereof.

A method for operating a front-end device by using an integrated SIP video monitoring system platform comprises an integrated SIP video monitoring system platform integrated with SIP Proxy server and WEB server. The integrated SIP video monitoring system platform provides the WEB page for access and login of the user terminal; after the user terminal logins the integrated SIP video monitoring system platform, the method builds a conversation connection between the integrated SIP video monitoring system platform and the SIP gateway of the front-end device by using the SIP request; the SIP gateway obtains the information operating the front-end device via analyzing the SDP message of the SIP message, and builds the instruction which can be received by the front-end device to operate the front-end device, and then returns the operated results from the front-end device to the integrated SIP video monitoring system platform. The method can achieve the functions of distribution, detection and video browsing and so on under the B/S (browsing end/service end) mode.

The invention discloses a motion control method for the autonomous berthing of an unmanned surface vessel and belongs to the unmanned surface vessel autonomous berthing motion control field. The method includes the following steps that: a current control system mode is determined according to the current berthing state of the unmanned surface vessel; the current actual speed or heading of the unmanned surface vessel is obtained, and the control deviation and deviation change rate of the heading or speed are obtained; with the control deviation and deviation change rate of the heading or the speed adopted as the input of a fuzzy controller, an appropriate control parameter variation quantity is selected on the basis of the current control system mode, and a control parameter is updated; andwith e(t) adopted as the input of the controller, and the controller outputs a desired control command to an actuator. According to the motion control method of the invention, improvement is made onthe basis of the traditional PID controller, and the controller has two modes, namely, a remote end mode and a nearshore mode; an adaptive fuzzy control rule is added, so that the controller can dynamically and adaptively adjust the control parameter according to berthing behavior change; and therefore, a motion control problem under the influence of weak maneuverability, large disturbance and a strong bank effect during the autonomous berthing of an under-driven unmanned surface vessel can be solved.