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150 results about "Bayesian framework" patented technology

A Bayesian Framework for Modeling Human Evaluations. Himabindu Lakkaraju Jure Leskovec Jon Kleinbergy Sendhil Mullainathanz. Abstract Several situations that we come across in our daily lives involve some form of evaluation: a process where an evaluator chooses a correct label for a given item.

Efficient tracking multiple objects through occlusion

Visual tracking of multiple objects in a crowded scene is critical for many applications include surveillance, video conference and human computer interaction. Complex interactions between objects result in partial or significant occlusions, making tracking a highly challenging problem. Presented is a novel efficient approach to tracking a varying number of objects through occlusion. The object tracking during occlusion is posed as a track-based segmentation problem in the joint-object space. Appearance models are used to interpret the foreground into multiple layer probabilistic masks in a Bayesian framework. The search for optimal segmentation solution is achieved by a greedy searching algorithm and integral image for real-time computing. Promising results on several challenging video surveillance sequences have been demonstrated.
Owner:FUJIFILM BUSINESS INNOVATION CORP

Method for detecting image significance

The invention belongs to the technical field of detection of image significance, which is characterized in that the significance target of any image can be detected, and relates to relevant knowledge of image processing. The method comprises the following steps of: firstly, over-segmenting an image into super pixels, and performing Harris interest point detection to form a convex hall; secondly, performing edge detection on the image and calculating the edge weight map of the image; thirdly, measuring color space information by using the edge weight image to obtain a prior image; fourthly, performing soft segmentation based on the prior image to obtain an observation likelihood probability; and lastly, combining the prior image with the observation likelihood probability by using a Bayesian framework to obtain a significance detection result. The method has the beneficial effects that background noise can be well eliminated, a high-brightness image target is smoothened, the situations of target color and background similarity, large targets and complex backgrounds which are always difficult to solve in significance detection can be handled, and the method can be well applied to ordinary images.
Owner:DALIAN UNIV OF TECH

System and method for image annotation and multi-modal image retrieval using probabilistic semantic models

ActiveUS7814040B1Easy to annotate and retrieveEvaluation lessMathematical modelsDigital data information retrievalAlgorithmCorresponding conditional
Systems and Methods for multi-modal or multimedia image retrieval are provided. Automatic image annotation is achieved based on a probabilistic semantic model in which visual features and textual words are connected via a hidden layer comprising the semantic concepts to be discovered, to explicitly exploit the synergy between the two modalities. The association of visual features and textual words is determined in a Bayesian framework to provide confidence of the association. A hidden concept layer which connects the visual feature(s) and the words is discovered by fitting a generative model to the training image and annotation words. An Expectation-Maximization (EM) based iterative learning procedure determines the conditional probabilities of the visual features and the textual words given a hidden concept class. Based on the discovered hidden concept layer and the corresponding conditional probabilities, the image annotation and the text-to-image retrieval are performed using the Bayesian framework.
Owner:THE RES FOUND OF STATE UNIV OF NEW YORK

Moving object detecting method based on Bayesian frame and LBP (Local Binary Pattern)

The invention discloses a moving object detecting method based on a Bayesian frame and an LBP (Local Binary Pattern), which relates to the technical field of intellectualized video monitoring. The method comprises the following steps of: 1. extracting a video frame in a video stream; 2. preprocessing the video frame and eliminating the interference of fine light ray change and other disturbance; 3. carrying out moving object detection based on a background model and the Bayesian frame; before detecting, filtering a pixel point with unchanged time difference and background difference and degrading color dimensionality in modeling, wherein the speed of the whole method is improved, and the background model comprises a color characteristic and a symbiotic color characteristic and can favorably detect a moving and static object in a video; and 4. removing a shadow, detecting the texture information of the obtained moving object area and the texture information of a background image corresponding area by utilizing an LBP descriptor and comparing the differences of the texture information for removing the shadow in moving object detection.
Owner:云南清眸科技有限公司

Coupled Bayesian Framework For Dual Energy Image Registration

A computer implemented method for joint image registration and reconstruction of a plurality of images includes providing the plurality of images, modeling the plurality of images, including reconstructing bone and soft-tissue in respective images of the plurality of images, performing a hierarchical free-form registration of models of the plurality of images to determine a jointly registered and reconstructed image with successive accuracy adjustment according to a registration error, and outputting the registered and reconstructed image.
Owner:SIEMENS MEDICAL SOLUTIONS USA INC

Monocular vision and laser radar fusion-based road travelable region detection method

ActiveCN107167811ALarge precision contributionRobustElectromagnetic wave reradiationComplex trainingLaser sensor
The invention discloses a monocular vision and laser radar fusion-based road travelable region detection method and belongs to the intelligent transportation field. Existing unmanned vehicle road detection methods are mainly based on methods such as monocular vision, stereo vision, laser sensor and multi-sensor fusion methods, have defects of low robustness to illumination, complex three-dimensional matching, laser sparseness, low overall fusion efficiency and the like. Although some supervised methods have achieved better accuracy, the training processes of the supervised methods are complex, and the generalization effects of the supervised methods are poor. According to the monocular vision and laser radar fusion-based road travelable region detection method provided by the present invention, ultra-pixel and point cloud data fusion is adopted; on the basis of features, road regions can be obtained through machine self learning; and the features are fused through the Bayesian frame, so that road information is obtained, and a final region can be obtained. With the method adopted, strong hypothesis information and complex training processes are not required. The monocular vision and laser radar fusion-based road travelable region detection method has the advantages of excellent generalization performance, high robustness, fast speed and high precision, and can be popularized and used more easily in practical application.
Owner:XI AN JIAOTONG UNIV

System and method for matching objects belonging to hierarchies

An improved system and method for matching objects belonging to hierarchies is provided and an optimal matching between two feature spaces organized as taxonomies may be learned. The matching may be performed through a multi-level exploration of the hierarchical feature spaces by using multi-armed bandits where the arms of the bandit may be dependent due to the structure induced by the taxonomies. Upon the arrival of an object assigned to the first taxonomy, multi-armed bandits may be run at multiple levels of the taxonomies to select an object assigned to the second taxonomy. Then shrinkage estimation may be performed in a Bayesian framework to exploit dependencies among the arms by estimating payoff probabilities from a beta-binomial model to update payoff probabilities for matching objects from the taxonomies.
Owner:OATH INC

Continuous and stable tracking method of weak moving target in dynamic background

ActiveCN106875415ARobust trackingAvoid update errorsImage enhancementImage analysisContext modelConfidence map
The invention discloses a continuous and stable tracking method of a weak moving target in a dynamic background. The method includes acquiring the video data, and processing each frame of image in the following steps of acquiring the position coordinate of the moving target to be tracked in the current frame of image and determining the target tracking frame according to the position; establishing a spatial context model of the current frame of image for the area in the target tracking frame with a Bayesian framework; performing convolution calculation by means of the spatial context model of the current frame of image and the next frame of image to obtain a confidence map of the position of the moving target to be tracked in the next frame of image, the position with the greatest confidence is the position of the moving target to be tracked in the next frame of image; based on the double threshold moving target crisis determination, determining that when the moving target to be tracked is not shielded or lost, outputting the moving target position in the next frame of image when the tracking of the current frame of image is completed; otherwise, updating the target tracking frame and re-checking. The method realizes the continuous and stable tracking of the target under the condition of background interference and shielding.
Owner:北京理工雷科电子信息技术有限公司

Bayesian saliency based SAR image target detection method

The invention discloses a Bayesian saliency based SAR image target detection method and mainly solves the problems of low detection accuracy and incomplete structure of a detected target on a binary image in an existing SAR image target detection technology. The method is implemented by the steps of performing superpixel segmentation on an original SAR image; constructing a prior saliency map, a foreground likelihood graph and a background likelihood graph by utilizing a superpixel segmentation result; fusing results of the prior saliency map, the foreground likelihood graph and the background likelihood graph in a Bayesian framework to obtain a Bayesian posterior saliency map; segmenting the Bayesian posterior saliency map to obtain a binary image with a suspected target region; and performing clustering and false alarm region removal on the binary image and extracting a suspected target slice on an original SAR image, thereby finishing the SAR image target detection. Compared with dual-parameter CFAR detection, the method has the advantages of high detection accuracy and relatively complete structure of the detected target on the binary image, thereby being suitable for SAR image target detection in complicated scenes.
Owner:XIDIAN UNIV

Constrained surface evolutions for prostate and bladder segmentation in CT images

InactiveUS20070014462A1Accurate and stable segmentationImage enhancementImage analysisMedicineBayesian formulation
A Bayesian formulation for coupled surface evolutions in level set methods and application to the segmentation of the prostate and the bladder in CT images are disclosed. A Bayesian framework imposing a shape constraint on the prostate is also disclosed, while coupling its shape extraction with that of the bladder. Constraining the segmentation process improves the extraction of both organs' shapes.
Owner:SIEMENS MEDICAL SOLUTIONS USA INC

Multi-target tracking method based on dynamic group division

The invention relates to a multi-target tracking method based on dynamic group division, which comprises the steps as follows: S1, for a certain time in a video scene, whether a target enters or leaves a scene area or not is judged firstly and a target set is updated according to a detection result; S2, a grouping threshold of the target set is set and hierarchical clustering is used to cluster the target set at last time by distances, and then a target position at last time is divided into different groups according to a clustering result at last time; S3, each group in the target set is provided with a tracker; the trackers use a Bayesian framework to deduce the optimal value of each target at a current time position according to the position state of each target at last time and the observation value of each target on an image at the current time in the groups; and the optimal value of each target at the current time position is used for updating the position state of each target in the target set so as to prepare deducing the position of each target at next time.
Owner:江苏瑞奥风软件科技有限公司

Image segmentation method having multi-layer segmentation networks based on Bayes framework with edge prior information

The invention relates to an image segmentation method having multi-layer segmentation networks based on Bayes framework with edge prior information. The method herein includes the following steps: S1.performing general classification on input data by using a full convolutional neural network, outputting scoring graphs of all types that have the same sizes of the input data, also extracting an implicit edge image from an internal feature layer of the full convolutional neural network; S2. extracting an explicit edge image from the input data by using an edge detection network; S3.performing first restraining on the types that are obtained from S2 by using domain transformation and conditional random field, obtaining an initial segmentation image; S4. transforming the explicit edge image obtained from S2 to an edge distance image; and S5. inputting the edge distance image to defined domain transformation, performing second edge restraining on the initial segmentation image that is obtained from S3, and obtaining a final segmentation result. According to the invention, the method can extract edge prior information through an external edge network and performs edge region segmentationand filtering on the result from the general segmentation by using the edge prior information, so that the method herein can increase the accuracy in segmenting a SAR image.
Owner:WUHAN UNIV

Mutual information regularized Bayesian framework for multiple image restoration

A method for multiple image restoration includes receiving a plurality of images corrupted by noise, and initializing a reduced noise estimate of the plurality of images. The method further includes estimating a probability of distributions of noise around each pixel and the probability of the signal, estimating mutual information between noise on the plurality of images based on the probabilities of distributions of noise around each pixel and the joint distribution of noise, and updating each pixel within a search range to determine a restored image by reducing the mutual information between the noise on the plurality of images.
Owner:SIEMENS MEDICAL SOLUTIONS USA INC

Data association method for utilizing sensing network to carry out multi-target tracking

The invention provides a data association method for utilizing a sensing network to carry out multi-target tracking. The method comprises the steps of: clustering sensor nodes; collecting measurement information of targets by the nodes; calculating state discrete time equations of the targets; calculating state prediction results of the targets; calculating the posterior probability of a target k in a node I; carrying out target tracking by means of a Bayes framework; calculating the posterior probability distribution of the targets; and repeating the above steps to finish target tracking. In order to carry out accurate tracking on multiple targets, the invention provides the new method to solve the data association problem in wireless sensor network multi-target tracking, the method is based on the Bayes framework, the target position state information is utilized, and the speed state information is also utilized; and compared with the modeling methods, the method provided by the invention is low in calculation amount, high in precision and is capable of accurately carrying out multi-target tracking under a low-cost condition especially in the aspect of cross and maneuvering target tracking.
Owner:STATE GRID CORP OF CHINA +2

Remote sensing image airport detection method based on weak supervised learning frame

The invention relates to a remote sensing image airport detection method based on a weak supervised learning frame. The method comprises the steps that firstly, the saliency of image blocks in a positive sample, the similarity of image blocks in a positive sample set and the inter-class difference between the positive sample and a negative sample are obtained, then, the Bayesian frame is utilized for fusing the three classes of information to obtain an initial positive and negative training set, then, iteration training of the training set is utilized for obtaining a final stable airport detector, the airport detector is used for detecting an airport of a tested image, and finally the airport detection result with better accuracy and robustness is obtained.
Owner:NORTHWESTERN POLYTECHNICAL UNIV

Unified bayesian framework for shape registration

Systems and methods for shape registration are described. In one aspect, training shape vectors are generated from images in an image database. The training shape vectors identify landmark points associated with one or more object types. A distribution of shape in the training shape vectors is represented as a prior of tangent shape in tangent shape space. The prior of tangent shape is then incorporated into a unified Bayesian framework for shape registration.
Owner:MICROSOFT TECH LICENSING LLC

Video tracking method based on local background learning

The invention provides a video tracking method based on local background learning. The video tracking method includes the steps that the time-space relationship between a target to be tracked and the local background of the target is modeled through the Bayes frame, a plurality of multi-dimensional images of the target are simultaneously collected through the time-space relationship between the modeled target and the local background, and dimensions of the collected multi-dimensional images of the target are reduced through a random sensing matrix meeting compressed sensing conditions to obtain feature vectors of the multiple multi-dimensional images; according to the feature vectors of the multiple multi-dimensional images, the multi-dimensional images with the dimensions reduced are classified through a naive Bayes classifier, and the position where the target appears is estimated according to a likelihood confidence image of the target position; based on target structure constraint conditions, a collector outputs the target with the maximum degree of overlapping with the previous frame target tracked successfully as the final tracking target. The video tracking method is suitable for video target tracking under complex conditions, and is high in discernment capacity and tracking accuracy.
Owner:SHANGHAI JIAO TONG UNIV

A knowledge base completion method based on variational interference and a tensor neural network

The invention discloses a knowledge base completion method based on variational interference and a tensor neural network. Tensor decomposition thought and a Bayesian framework are introduced into the knowledge base completion method, and the prior knowledge of implicit variables is considered. The interaction between the implicit variables is discussed and nonlinear expression is performed through the neural network, and consideration of nondeterminacy is added. The accuracy of the knowledge base method is improved obviously, and compared with the existing technology, the knowledge base completion method has been greatly improved.
Owner:UNIV OF ELECTRONIC SCI & TECH OF CHINA

Hand area segmentation method deeply integrating significance detection and prior knowledge

The invention relates to a hand area segmentation method deeply integrating significance detection and prior knowledge. This method combines the significant pattern of a hand area at pixel level with the significant pattern of a hand area at the regional level to make the detection algorithm for the entire hand area achieve higher robustness and accuracy. The method comprises the following steps: using an introduced Bayesian framework to obtain the confidence degree of each pixel in the hand area; combining the relevant techniques such as threshold segmentation, and finally obtaining a hand area segmentation result with high accuracy. The invention overcomes the shortcomings of a traditional hand area method which can only be applied to relatively simple background and non-near-skin color interference scene, and the method can obtain a hand area segmentation image accurately even in the case of various disturbances such as non-uniform illumination, near skin color background and face noise, therefore, making it applied with great prospects.
Owner:SHANDONG UNIV

Recognition method for simultaneously inverting reservoir fluid and brittleness based on porous medium model

The invention discloses a recognition method for simultaneously inverting reservoir fluid and brittleness based on a porous medium model, and provides an AVO parametric equation and an inversion method based on a Bayesian framework to reflect the product of fluid equivalent bulk modulus and Young modulus and density. The fluid equivalent bulk modulus has enough sensitivity for determining the property of the fluid, the fluid equivalent bulk modulus can overcome the influence of porosity through linear change of water saturation, and the fluid recognition multiplicity is reduced. The Young modulus and the density of the brittle reservoir have relatively high values, and the product of the Young modulus and the density can highlight the brittleness of the reservoir; and the two parameters are obtained at the same time by using a new parameterized AVO approximation formula, so that the consistency of inversion results is ensured, and the calculated amount is reduced. According to the method, fluid recognition and brittleness evaluation are comprehensively considered, reliable support is provided for reservoir fluid prediction and subsequent development, and good practical applicationvalue is achieved.
Owner:TAIYUAN UNIV OF TECH

Video super-resolution method based on compressed domain

The invention discloses a video super-resolution method applicable to a compressed domain, which fully utilizes the information of a plurality of previous and next frames for the purpose of the super-resolution reconstruction of a target frame. The method mainly comprises the following steps of: firstly, uncompressing a low-resolution video to obtain various information; then respectively obtaining various single super-resolution images in a current window by using the obtained information and a Bayesian framework; and finally, reconstructing a final super-resolution image of the target frameby utilizing the single super-resolution images in the current window. During such processes, the method provides a gradient distribution that the gradient distribution of the super-resolution image is as close as practicable to uncompressed low-resolution images, and takes the provided gradient distribution as prior probability; and the method utilizes IRLS to construct a target function, therebyweakening the influence from outliers and noise, leading the obtained super-resolution image to be higher in PSNR, introducing little new noise and inhibiting coding noise and ringing effect. By sequentially sliding mobile windows, a super-resolution video can be obtained.
Owner:ZHEJIANG UNIV

Multi-maneuvering target tracking method based on iteration adaptation

The invention relates to a multi-maneuvering target tracking method based on iteration adaptation. According to t he invention, based on a data association problem and a target maneuvering problem in multi-maneuvering target tracking processes, a confidence propagation algorithm and a jump Markov system fixed interval smoother are used; under the unified framework of the variational Bayesian, united optimization is performed on target states and association matrixes through an iteration mechanism, so tracking precision of the maneuvering target is improved; and compared with the current multi-maneuvering target tracking algorithm, the provided method is characterized in that based on the iteration feedback idea, the data association problem and the target maneuvering problem are processed in an united manner under the unified variational Bayesian framework so the method has certain theoretical and actual engineering meanings in the target tracking field.
Owner:NORTHWESTERN POLYTECHNICAL UNIV

Seismic random inversion method and device based on multi-point geostatistical prior information

The invention discloses a seismic random inversion method and device based on multi-point geostatistical prior information. The method comprises the following steps of determining a to-be-inverted profile, well data and a training image according to a known three-dimensional geological model; determining a rock phase probability distribution model of the to-be-inverted profile according to the well data and the training image; under the constraint of the rock phase probability distribution model, determining a prior probability density function of elastic parameters corresponding to a to-be-inverted seismic trace on the to-be-inverted profile according to the well data and a probability density function and a variation function of the elastic parameters corresponding to different rock phases obtained by performing petrophysical statistics on the well data; and in the Bayesian framework, determining an inversion result according to the prior probability density function of the elastic parameters obtained under the constraint of the rocky phase probability distribution model and seismic data. The seismic random inversion method and device provide more accurate prior information for inversion, are beneficial to improve the accuracy and resolution of the inversion result, and make the inversion result more in line with actual production requirements.
Owner:BC P INC CHINA NAT PETROLEUM CORP +1

Compressed sensing image reconstruction method based on relevance vector grouping

The invention discloses a compressed sensing image reconstruction method based on relevance vector grouping, which mainly solves the problems of inaccuracy and low robustness of compressed sensing image reconstruction. The realization process is as follows: 1) receiving an observation matrix and an observation vector; 2) obtaining an initial relevance vector by the observation vector and a sending matrix; 3) dividing the relevance vector into sub-relevance vectors according to the spatial neighbourhood relationship of wavelet coefficients; 4) adding a component in each sub-relevance vector and sequencing the components; 5) updating the reconstructed wavelet high-frequency coefficients and observation vectors on the basis of a Bayesian framework according to the sequencing order; 6) carrying out invert wavelet transform on the reserved low-frequency wavelet decomposition coefficients and the reconstructed high-frequency wavelet coefficients to obtain a reconstructed image. Compared with OMP and BEPA methods, the compressed sensing image reconstruction method based on relevance vector grouping disclosed by the invention has the advantages of high quality and good robustness of the reconstructed image, and can be used for reconstruction for natural images and medical images.
Owner:XIDIAN UNIV

Block sparse compressive sensing-based infrared image reconstruction method and system thereof

The present invention provides a block sparse compressive sensing based infrared image reconstruction method the method comprises : the step of block sparse representation, the continuous infrared small target image sequence is shown having a block sparse characteristics of the signals; the time correlation modeling step in bayesian framework infrared small target for continuous the data between the image sequence for modeling time correlation; step of outputting a signal reconstruction, sparse learning algorithm is adopted to realize the reconstruction of the infrared small target image, and a final estimated value of the output signal of the present invention also provides a block sparse compressive sensing based infrared image reconstruction system. The technical scheme provided by the invention in the compressed sensing reconstruction process, utilizing a continuous signal in time correlation signals are reconstructed, the single frame signals can be improved, and the reconstruction accuracy significantly reduces the run time of the algorithm.
Owner:SHENZHEN UNIV

Variational Bayesian self-adaptive filtering method

The invention relates to a variational Bayesian self-adaptive filtering method. For the target status estimation problem in a case of unknown process noise time-varying, inverse Wishart distribution is used for a priori modeling on process noise covariance through introducing a new hidden variable, and target status, the process noise covariance and the hidden variable are jointly optimized undera variational Bayesian framework through an iterative mechanism to improve tracking accuracy of the target status. Compared with the existing filtering algorithms, the method of the invention directlyselects the conjugate a priori distribution of the process noise covariance, carries out iterative optimization of a target status estimation result under the variational Bayesian framework, obtainsa new self-adaptive filtering algorithm, and has certain theoretical and practical engineering significance for the field of filters.
Owner:NORTHWESTERN POLYTECHNICAL UNIV

Salient image extraction processing method and system

The invention discloses a salient image extraction processing method and system. The method comprises the steps: analyzing the characteristics of the current image through an RGB channel so as to obtain the RGB salient characteristics of the current image; analyzing the characteristics of the current image through the Depth channel so as to obtain the Depth salient characteristics of the current image; both the RGB salient characteristics and the Depth salient characteristics meet conditional independent distribution and are assumed to obey Gaussian distribution; and performing salient characteristic fusion based on the Bayesian framework to estimate the saliency posterior probability so as to obtain the image saliency area. Therefore, the invention has the beneficial effects that: the high-level saliency characteristics of the RGB image and the Depth image are extracted by using the deep convolutional neural network, the correlation of the saliency characteristics is analyzed, the saliency characteristics are fused under the Bayesian framework and 3D saliency detection is modeled by using the DMNB generation model so as to obtain good accuracy, recall rate and F-measure.
Owner:NEW TECH APPL INST BEIJING CITY

Maximum posterior probability-based super-resolution method of airborne foresight sea surface target angle

The invention provides a maximum posterior probability-based super resolution method of an airborne foresight sea surface target angle, belonging to the field of radar imaging and signal processing. According to the method, a traditional range high resolution technology is used on the basis of establishing a radar echo signal two-dimensional convolution model, namely high resolution of radar echorange is realized by pulse compression and range migration correction technology; in the azimuth, radar angle super-resolution imaging is realized by adopting a sea-surface target retrieval method. According to the statistic distribution characteristic of sea-surface clutter, the sea-surface clutter distribution is represented by adopting rayleigh distribution, and the sea-surface distribution characteristic is expressed by utilizing distribution which has an adjustable characteristic based on generalized gaussian distribution, and the MAP iterative solution is exported under the Bayesian framework to realize sea-surface target angle super-resolution. The method has the advantages of low calculation complexity, strong imaging adaptability under complicated sea state and different target scenes.
Owner:UNIV OF ELECTRONICS SCI & TECH OF CHINA

Orthotropic dielectric fluid factor and fracture parameter inversion method.

The invention provides an orthotropic dielectric fluid factor and fracture parameter inversion method. The method comprises the following steps: deducing a rigidity matrix of an ortho-symmetric anisotropic fracture medium by using a vertical transverse isotropic medium under a dry rock background according to fracture weakness; with utilization of hypotheses of weak anisotropy and small fracture weakness, deducing a new expression of weak anisotropy approximate rigidity of a saturated fluid rock in an orthogonal medium by combining an anisotropy Gassmann equation; acquiring a PP wave linearized reflection coefficient for fluid and fracture parameter decoupling in the orthogonally symmetrical weak anisotropic medium by combining stiffness disturbance and scattering theories; and with logging information as prior information, realizing elastic impedance pre-stack inversion changing with offset and azimuth angle by using a partial incident angle stacked by azimuth seismic data under Bayesian framework. According to the invention, Reliable results can be provided for fluid identification and fracture characterization; and technical supports are provided for seismic wave propagation research, oil and gas development and seismic disaster prevention.
Owner:CENT SOUTH UNIV
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