156results about How to "Remove redundant information" patented technology

A system and method for a vulnerability assessment mechanism that serves to actively scan for vulnerabilities on a continuous basis and interpret the resulting traffic in context of policy is provided. Vulnerability information is presented within an enterprise manager system enabling the user to access vulnerability information, recommended remediation procedures, and associated network traffic. A studio mechanism is used to add scanners to the appropriate policies and control the scope and distribution of scans within the target network.

A method for reading data in a block-level single-instance storage system may involve receiving a first address of a data block, retrieving a signature corresponding to the first address, and reading data from a second address corresponding to the signature. A storage system may include a storage manager and first and second lookup tables. The storage manager may interface with an application (such as a database system or a file system) that uses a first set of identifiers for data blocks. The storage manager may use a second set of identifiers for the data blocks, and translates between the first and second identifiers using the lookup tables. The first lookup table indexes data block signatures according to the first set of identifiers. The second lookup table indexes the second set of identifiers according to the data block signatures. The second lookup table may be pruned to provide single instance storage.

The invention requests to protect an identification method for a human facial expression based on two-step dimensionality reduction and parallel feature fusion. The adopted two-step dimensionality method comprises the following steps: firstly, respectively performing the first-time dimensionality reduction on two kinds of human facial expression features to be fused in the real number field by using a principal component analysis (PCA) method, then performing the parallel feature fusion on the features subjected to dimensionality reduction in a unitary space, secondly, providing a hybrid discriminant analysis (HDA) method based on the unitary space as a feature dimensionality reduction method of the unitary space, respectively extracting two kinds of features of a local binary pattern (LBP) and a Gabor wavelet, combining dimensionality reduction frameworks in two steps, and finally, classifying and training by adopting a support vector machine (SVM). According to the method, the dimensions of the parallel fusion features can be effectively reduced; besides, the identification for six kinds of human facial expressions is realized and the identification rate is effectively improved; the defects existing in the identification method for serial feature fusion and single feature expression can be avoided; the method can be widely applied to the fields of mode identification such as safe video monitoring of public places, safe driving monitoring of vehicles, psychological study and medical monitoring.

A method for creating, storing and reading a new certificate type for certification of keys is provided. In the new certificate type, several certificates, containing a minimum quantity of redundant data fields, are collated to form one certificate and all redundant information on the certificates is eliminated. An embodiment of the new certificate type is the group certificate. The group certificate is used where several keys are to be issued at the same time for the same user by the same certification instance. By means of the group certificate, all redundant data elements are eliminated and all data elements for a set of several keys subject to certification are grouped into one certificate. This substantially reduces the memory requirement, and handling of the certificates is simplified for the communication partners. A further embodiment of the new certificate type is the basic and supplementary certificate combination. This form of certification is used where certificates are issued at different times for the same user by the same certification body. The memory requirement is consequently somewhat more than for group certificates, but greater flexibility is gained in use of the keys.

The invention provides a multi-scale gradient domain image fusion algorithm which is used for fusing infrared thermal images with visible light images. The multi-scale gradient domain image fusion algorithm is characterized by comprising the following steps: a step of constructing gradient fields; a step of correcting gradient fusion weight; a step of reconstructing gradient fields; and a step of fusing images through a transparency method. The multi-scale gradient domain image fusion algorithm provided by the invention can be used for simultaneously reserving temperature rise areas and detail information of infrared thermal images and visible light images, removing a halation background-effect phenomenon and redundant information due to interaction among images and avoiding color inconsistency of an infrared thermal image temperature rise area.

The invention belongs to the application fields of seismic data oil gas predication, oil gas resource exploration and signal processing, and provides a Contourlet transformation based seismic multi-attribute fusion method. Effectiveness of description attribute information is improved by means of expression of multiresolution, multidirection, localization and anisotropy of signals to make full use of existing various attribute information, accuracy in seismic target calibration is improved, and accordingly reservoir predication and fluid recognition can be completed more accurately.

A facial feature extraction method with light robustness includes the following procedures: pre-treatment is conducted on a human face image by utilizing edge detection or discrete fourier transformation; after uniform non-overlapping blocking of the pretreated human face image, the row and the column variance projection entropies of each block are calculated and the row and the column variance projection entropies of each block are integrated into human facial feature vectors to construct a feature database of a plurality of human face images. In recognition authentication, according to the same methods of pretreatment and facial feature vector extraction, facial feature vectors to be recognized are computed and distance classification is performed on the computed vectors and facial image feature vectors in the feature database one by one to get the recognition results. The invention has the advantages that complicated lighting models and imaging devices are not needed; the method has excellent robustness to lighting and can extract features needed for face recognition with a high recognition rate even for facial images formed under poor lighting conditions; the method has fast processing speed and the ability of real-time treatment.

A header compression / decompression apparatus that improves the throughput of an overall multilayer protocol stack at a network node. In this apparatus, an encoding section 106 compresses multilayer header information included in a protocol data unit on a multilayer protocol stack 101. A session context ID manager 112 generates a session context ID 401 which is formed by integrating information on compression of multilayer header information by the encoding section 106 and, for example, information for identifying a scheme for compressing multilayer header information.

According to the present invention, an image coding apparatus (103) that encodes quantized coefficients corresponding to an image signal as target data to be processed is provided with a run-length encoding unit (RLE2) that assigns variable length codes to the quantized coefficients using code tables. The run-length encoding unit (RLE2) forms a second code table by optimizing a first code table to the target data to be processed, and selects one of the first and the second code tables as a code table that is to be employed for the assignment of the variable length codes in accordance with a quantization parameter (QP) or a variable length coding selection signal (VlcSel), whereby redundancy of information included in the target data to be processed can be effectively eliminated, and the compression ratio for the image signal or the like can be further increased.

The invention discloses a three-dimensional video encoding method based on depth image rendering. The encoding method disclosed by the invention comprises the following steps of: projecting an original left-viewpoint colorful image to a right viewpoint from a left viewpoint through a method based on the depth image rendering, so as to obtain a residual image of an rendered image of an original right-viewpoint colorful image and the original left-viewpoint colorful image; carrying out pre-processing operation on the residual image and encoding the original left-viewpoint colorful image and the pre-processed residual image; and finally, obtaining a finally-reconstructed image of the decoded right-viewpoint colorful image according to the rendered image of the decoded right-viewpoint colorful image and the decoded residual image. According to the invention, on the premise of ensuring the higher reconstruction quality of the right-viewpoint colorful image, the encoding efficiency of a three-dimensional video is greatly improved.

The invention discloses a turning chatter detection method, and relates to the technical field of detection. In the turning process, the state of a machine tool can be reflected in dynamic cutting force. The turning chatter detection method includes the steps that firstly, an off-line data training model is used, force signals are decomposed to a sixth layer through wavelet packet transformation, energy of each node is worked out, and a 64-dimension feature vector is obtained; dimensionality reduction is conducted on the feature vector through least squares support vector machine-regression feature elimination (LSSVM-RFE), redundancy features are eliminated continuously, optimal features are selected out, and a least squares support vector machine classifier is trained according to the optimal features; and each selected feature corresponds to one wavelet packet node, in the on-line detection process, only a small wavelet packet matrix is needed to decompose force signals to the small wavelet packet nodes selected in the off-line training process, the feature vector is built and input into the classifier, and a detection result is obtained. By the adoption of the dimensionality reduction method, the turning chatter detection method has the beneficial effects of being high in speed and high in identifying accuracy and effectively guaranteeing the machining safety and the product quality.

The present invention relates to a consistent iteration and multi-view transfer learning-based pedestrian re-identification method. The method comprises the following steps that: feature extraction is performed on pedestrian images, local and global multi-view image visual terms are obtained; a transfer learning method and a discriminatory analysis method are adopted to construct a consistent iteration and multi-view transfer learning optimization model, and the model is solved, so that middle-level image feature descriptors are obtained; calculation is performed based on obtained low-level feature descriptors and the middle-level feature descriptors, so that final multi-level image feature descriptors can be obtained; and a cross-view-based secondary discriminatory analysis method is utilized to measure the similarity of pedestrians, so that the similarity sequencing result of the pedestrian images can be obtained. Compared with the prior art, the consistent iteration and multi-view transfer learning-based pedestrian re-identification method of the invention has the advantages of high robustness and reliability under the change of factors such as illumination and rotation generated under a multi-view condition, can extract the bottom-level and middle-level feature descriptors of the images and has high pedestrian identification capability.

A method and system of optimizing exceptions to default timing constraints for use in integrated circuit design tools is described. A list of exceptions is accessed and optimized to generate a new list of exceptions. Optimizations may include: elimination of redundant information, resolution of conflicting information, and other transformations. The new list allows more efficient timing analysis, synthesis, placement, routing, noise analysis, power analysis, reliability analysis, and other operations to be performed by EDA tools.

The invention discloses an intelligent fire detection method based on a WSN. The method includes the following analyzes the application features of a wireless sensor network and analyzing the non-structural features of how a fire happens, and in combination with a general function model of the data fusion technology, and establishes a multi-layer and graded fire detection model. The model adopts the WSN which is composed of 3types of sensors. Firstly, the acquired original data undergoes data level processing so as to standardize the data, and the data undergoes special level processing so asto remove redundant information from a network and increase the precision in identifying a fire and the robustness of the WSN. According to the invention, the method herein sufficiently takes the features of the wireless sensor network into consideration, makes sure that a fire early warning is sent in an in-time and accurate manner, makes systems more suitable for the environment, makes the decision-making of a fire fighting monitoring system more scientific, and is very significant and practical for the early warnings and forecasting of fire hazards.

A multi-scale OGLPE feature extraction method applied to inverter faults comprises the following steps of S1, collecting the original three-phase current signals, and obtaining the current deviation signals by combining with reference current signals; S2, performing multi-scale decomposition on the current deviation signal under each state by using an integrated empirical mode decomposition (EEMD)method to obtain intrinsic mode IMF components under different scales; S3, adaptively extracting sensitive features embedded in each IMF component by using an orthogonal global and local preserving embedded OGLPE feature extraction method to form a fault feature set. According to the method, the problems of insufficient functionality and limitation caused by only considering global distribution characteristics or local structure retention characteristics in the prior art are solved.

A case information optimization and acquisition method for a medical system based on a cloud platform relates to the field of information memory and safety. The medical information of all patients of a plurality of hospitals are stored in a database of a cloud server and aggregated, so that under the condition that the patients authorize, any hospital can use a client terminal to access the database of the cloud server and obtain the medical information of the patients. The medical information of all the hospitals is uploaded and aggregated by grades and finally aggregated into a cloud main database. If a patient sees a doctor at the same hospital for multiple times, the patient only needs to fill the personal basic information at the first time, and for the rest times, the patient can use a patient ID card uniquely identifying the identity of the patient. The time that the patient registers, queues up and fills the personal basic information is shortened, and the screening step of screening stored information is also reduced. The information exchange of all the hospitals and all departments is realized, and the 'information isolated island' problem is solved.

The invention provides a human-computer interaction method and a human-computer interaction device based on the integration of eye movement tracking and gesture recognition in a virtual assembly. Themethod comprises the steps: carrying out the gaze point tracking according to the obtained eye movement data; performing gesture recognition according to the obtained gesture information, labeling theobtained gesture recognition data and eye movement data to form a training set, and constructing a multi-stream convolutional neural network-long-term and short-term memory network model, wherein thenetwork model performs self-learning by using the training set; and applying the optimal network model obtained by training to a virtual assembly process, obtaining eye movement data and gesture information of the virtual assembly process, extracting eye movement and gesture features, and analyzing according to the feature information to obtain a behavior category of an operator so as to completean assembly task. The problem of misjudgment of similar behaviors in a single mode is solved. The advantages of a deep learning algorithm are utilized. The behaviors of operators in a video are recognized with high accuracy. A virtual assembly task is completed, and the man-machine interaction is achieved.

The invention discloses a face video compression method and belongs to the technical field of face video data compression in the case of multimedia communication. The method is characterized by comprising steps: a face model is used for carrying out positioning and parametric characterization on the face in the video at the sending end, a face model parameter vector represented by an illumination parameter vector, a gesture parameter vector and a shape and appearance combined parameter vector is obtained, constraint that global minimal parameter error and model parameter total bit are less than the given upper limit on the basis of the given to-be-retained parameter frame sample number is carried out, the face model parameter vector after optimal adaptive interframe compression is sent to a receiving end, an independent slicing linear interpolation method is used for restoring the face model parameter vector, and the original image is obtained through face shape calculation and restored face appearance. The face video compression ratio is greatly improved while the optimal reconstruction quality is ensured, redundancy in the face video is removed to the maximal degree in the time domain, and the communication resource utilization rate is thus improved.

The invention discloses a nondestructive testing method and system of fatty acid content of rice. The method comprises the steps that indica rice which is free of mildew, ungerminated and with the thousand kernel weight of 23.32 g is placed in a constant temperature and humidity incubator to conduct mould culture, and rice samples at different mildew periods are prepared; the reflectivities of the rice samples correspond to light waves with the wavelength of 392 nm, 404 nm, 430 nm, 442 nm, 619 nm, 636 nm, 870 nm, 885 nm and 899 nm are collected respectively; the reflectivities are put in a rice fatty acid content prediction model. According to the nondestructive testing method and system of the fatty acid content of the rice, the SPXY-SPA-MLR model which is built based on the characteristic spectrum waveband reflectivities can rapidly conduct nondestructive testing on the fatty acid content of the rice, the testing time is greatly shortened, and the testing cost is reduced.

The invention discloses an image edge detection method based on neighbourhood dispersion. The image edge detection method comprises the following steps: 1) transforming an original colorful image into a single-channel eight-bit gray level image; 2) obtaining the dispersion coefficient matrix M of the eight-bit gray level image; 3) executing a horizontal direction refining operation for the dispersion coefficient matrix M to obtain a matrix Mh; 4) executing a vertical direction refining operation for the dispersion coefficient matrix M to obtain a matrix Mv; 5) combining the matrix Mh with the matrix Mv, and combining all points which are equal to 1 in the two matrixes to obtain a rough refining edge matrix O; and 6) carrying out precise processing and redundant information filtering on an image outline by the rough refining edge matrix O according to an orientation gradient to obtain an outline matrix, and finally, carrying out transformation through the outline matrix to obtain a binary edge image. The method disclosed in the invention is simple and easy in implementation and guarantees the accuracy of the outline.

The invention discloses a method for discriminating corn haploid kernels based on a near-infrared spectrum technology. The method comprises the following steps of: reading into an original spectrum file and acquiring spectrum intensity data; dividing data sets; carrying out feature normalization on spectrum data; adopting a partial least-squares regression method to reduce the dimensionality; adjusting parameters of a neural network classifier; carrying out fine adjustment and performance evaluation on the neural network classifier; and storing the parameters of the neutral network. The method disclosed by the invention has the advantages that the corn haploid kernels can be identified from heterozygote kernels, and simultaneously higher correct discrimination rate and model stability are ensured.

The invention relates to an electromechanical equipment-oriented remote dynamic adaptive rule acquisition method. The electromechanical equipment-oriented remote dynamic adaptive rule acquisition method comprises the steps as follows: (1) acquiring state signals of function parts of electromechanical equipment by a sensor group; (2) uploading the state signals into a remote data system through a remote network, converting and storing the state signals in a unified data format; (3) transmitting the data of the state signals to a fault diagnosis module of a remote network platform, obtaining intrinsic mode functions representing the characteristics of the state signals by a variable scale empirical mode decomposition method, and carrying out Hilbert conversion on the intrinsic mode functions respectively so as to obtain corresponding instantaneous frequencies; and (4) constructing a remote fault diagnosis knowledge base system, obtaining a rule base composed of rules, and transmitting to a tool fault diagnosis and prediction service platform through the remote network; and (5) carrying out dynamic adaptive optimization updating on the obtained rule base according to a dynamic coordination method by the tool fault diagnosis and prediction service platform, and taking degree of confidence as evaluation of the rules.

The invention discloses a filter design based light flow vector micro-expression generation phase detecting method. The method includes steps of (1) performing decentralization and normalization on light flow motion features; (2) adopting a signal attenuation method for positioning; (3) performing micro-expression automatic detection and identification. The invention has beneficial effects that human face key point areas relating to muscle motion when the micro-expressions generate can be adopted in a targeted manner such that effective motion features can be extracted effectively and redundant information unrelated to micro-expression generation can be eliminated; decentralization and normalization are performed on the light flow motion features such that influence caused by distance between a photographing object and a lens and different human face shapes can be weakened effectively and errors caused by head integral motion can be eliminated; start frame and end frame positioning ofindicators are designed referring to a filter and a uniform standard is set for micro-expression detection and can be applied to different databases; a detection system can visualize the whole detection process practically and simple and easy operation is achieved.

The invention discloses a time-space combination prediction method based on CNN-LSTM and deep learning, and the method comprises the steps: S1, carrying out the analysis of the time and space correlation of an original data set through an MI mutual information algorithm, eliminating the redundant information between data, and carrying out the dimension reduction of the original data; S2, establishing an MI-CNN-LSTM model, and predicting the data after dimension reduction, wherein CNN is used for extracting spatial information of each station, and LSTM is used for obtaining dependency information among time sequence data; S3, adding an AT layer between an LSTM layer and an output layer of the LSTM network to obtain an MI-CNN-ALSTM model, and performing model learning on training set data; S4, introducing a PSO algorithm to optimize the training parameters, and obtaining an MI-CNN-ALSTM-PSO model; S5, predicting the test set to obtain a final predicted value. According to the MI-CNN-ALSTM-PSO space-time combination prediction model provided by the invention, the wind power prediction precision and stability are further improved.

The object of the invention is a method of processing information contained in an image, including:a first processing (1-4), for defining an area of interest of the image (8),effecting an adaptive thresholding (1-8) of this area of interest in order to obtain a threshold image (10) of this area of interest, referred to as the first thresholded image,the segmentation (1-12) of the thresholded image, in order to obtain a first set of morphological layers (14-1, 14-2, 14-3, . . . ) of the thresholded image.