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127 results about "Scale size" patented technology

Scale Size 1:43 Scale is a model approximately 3 to 5 inches in length ... 1:18 scale is a model approximately 8 to 11 inches in length 1:12 scale is a model approximately 14 to 16 inches in length

Multi-scale small object detection method based on deep-learning hierarchical feature fusion

The invention relates to the object verification technology in the machine vision field, and especially relates to a multi-scale small object detection method based on deep-learning hierarchical feature fusion; for solving the defects that the existing object detection is low in detection precision under real scene, constrained by scale size and different for small object detection, the invention puts forward a multi-scale small object detection method based on deep-learning hierarchical feature fusion. The detection method comprises the following steps: taking an image under the real scene as a research object, extracting the feature of the input image by constructing the convolution neural network, producing less candidate regions by using a candidate region generation network, and then mapping candidate region to a feature image generated by the convolution neural network to obtain the feature of each candidate region, obtaining the feature with fixed size and fixed dimension after passing a pooling layer to input to the full-connecting layer, wherein two branches behind the full-connecting layer respectively output the recognition type and the returned position. The method disclosed by the invention is suitable for the object verification in the machine vision field.
Owner:HARBIN INST OF TECH

Three-dimensional vector real-time dynamic stacking technique based on LOD (Level of Detail) transparent textures

The invention discloses a three-dimensional vector real-time dynamic stacking technique based on multiple LOD (Level of Detail) transparent textures. The three-dimensional vector real-time dynamic stacking technique comprises the following steps of: firstly, drawing various vector layers on the transparent textures of corresponding plotting scales respectively in an internal memory through data pre-processing, automatically carrying out block cutting on the vector layers, then saving the vector layers in a disk as an LOD pyramid data storage structure, and establishing a mapping relation with a same-level terrain and an image data block; and secondly, in a real-time terrain rendering and roaming stage, loading the transparent textures of vectors of the corresponding layers through dynamic selection, stacking the vector layers into the same level of image data block according to an established Alpha passage, and realizing the real-time stacking of the vector layers in the three-dimensional terrain based on an internal memory multi-passage texture blending technology. According to seamless visualization of high-resolution vector map data and a digital elevation model, the effect of the high-resolution vector map data and the digital elevation model in spatial information expression and analysis can be enhanced. According to the technique, the real time stacking of multilayer vector data in the rendering and roaming stage of a large-scale three-dimensional terrain scene is realized, and the dynamic interactive screening of the vector layers is supported.
Owner:北京峰盛博远科技股份有限公司

A multi-layer convolution feature self-adaptive fusion moving target tracking method

The invention relates to a multi-layer convolution feature self-adaptive fusion moving target tracking method, and belongs to the field of computer vision. The method comprises the following steps: firstly, initializing a target area in a first frame of image, and utilizing a trained deep network framework VGG-19 to extract first and fifth layers of convolution features of the target image block,and obtaining two templates through learning and training of a related filter; Secondly, extracting features of a detection sample from the prediction position and the scale size of the next frame andthe previous frame of target, and carrying out convolution on the features of the detection sample and the two templates of the previous frame to obtain a response graph of the two-layer features; calculating the weight of the obtained response graph according to an APCE measurement method, and adaptively weighting and fusing the response graph to determine the final position of the target; And after the position is determined, estimating the target optimal scale by extracting the directional gradient histogram features of the multiple scales of the target. According to the method, the targetis positioned more accurately, and the tracking precision is improved.
Owner:KUNMING UNIV OF SCI & TECH

Distributed type multi-robot synchronous swarming control method

The invention relates to a distributed type multi-robot synchronous swarming control method. The method comprises the steps of: identifying not more than six key neighbor robots in all the neighbor robots with the current position of a robot as a center, the detection distance of a sensor as a radius, and other robots in the circular area as neighbor robots, and designing a position control operator of the current robot by using an attraction-repulsion function among the current robot and the neighbor robots; designing a speed direction control operator of the current robot according to the linearization expression of a Vicsel model; and weighing and integrating the position control operator and the speed direction control operator to form a synchronous swarming control rate of the current robot. The invention visually demonstrates network topology and control action relations among robot positions, breaks the restriction on the scale size of robots, realizes purely distributed type extensible coordination control, guarantees communication of network topology structures in the process of group evolution, and can adjust parameters to avoid collision when the scale of the neighbor robots increases continuously and realize self-adapting to the change of the position relations of the robots.
Owner:EAST CHINA JIAOTONG UNIVERSITY

Concrete crack identification method based on YOLOv3 deep learning

The invention belongs to the technical field of concrete structure damage detection, and discloses a multi-target crack recognition method based on a YOLOv3 deep learning algorithm, which comprises the following steps: importing a crack image into a YOLOv3 model, and automatically compressing the image into 416 * 416 pixel resolution; dividing the original image into S * S grids according to the scale size of the feature map by adopting an up-sampling and feature fusion mode similar to FPN; taking the cross-to-parallel ratio of the candidate box and the real box as an evaluation criterion, and; performing K-means clustering analysis on mark boxes for all crack target marking boxes of the image training set to obtain the size of a candidate box; and predicting the probability that the frame contains the target for each boundary frame through logistic regression. According to the method, the complexity of network training is simplified, and the operation cost is reduced; according to the method, the multiple targets are quickly and accurately identified, the accuracy far superior to that of other models is obtained while the target detection is quickly realized, and the method has higher robustness and generalization capability and is more suitable for an engineering application environment.
Owner:ZHEJIANG UNIV

Method for using GPS and crosslink signals to correct ionospheric errors in space navigation solutions

A method of correcting ionospheric delays induced in received signals by space systems is disclosed. The method takes advantage of received GPS signals and received crosslink signals among spacecraft to estimate the effect of ionospheric delays and correct for such delays in the computation of the range estimation between spacecraft. The method generates and initial estimate of the ionospheric delay by tracking pseudorandom codes on both GPS and crosslink signals at known frequencies to correct an initial relative range vector. Using the corrected range vector generated from the use of code, the method subsequently estimates a more precise correction by considering the carrier phase error as induced by ionospheric delay. This includes estimate the integer ambiguities on both the GPS signals and the crosslink signals iteratively and subsequently estimating a more precise ionospheric delay correction with is applied to the relative position vector using the carrier phase measurements. The method is also applicable to non-navigation applications including measuring dynamic ionospheric structure and variability over a wide range of scale sizes, thereby greatly improving operational models of navigation and communications, and improving interdependent models of atmospheric, ionospheric, magnetospheric, and space weather physics and prediction.
Owner:THE JOHN HOPKINS UNIV SCHOOL OF MEDICINE

Preparation method of cobaltosic oxide-carbon porous nanofiber and application of cobaltosic oxide-carbon porous nanofiber to preparation of lithium ion battery

The invention discloses a preparation method of a cobaltosic oxide-carbon porous nanofiber and application of the cobaltosic oxide-carbon porous nanofiber to preparation of a lithium ion battery. The preparation method comprises the steps of dropwise adding a sol solution of soluble cobalt salt into a dimethylformamide solution of polyacrylonitrile and polymethyl methacrylate, continuously stirring, and keeping at the temperature of 80-100 DEG C for 24 hours; preparing a polymer/cobalt salt composite fiber supported on aluminum foil by using an electrostatic spinning process; heating the polymer/cobalt salt composite fiber to 280 DEG C at the rate of 2-4 DEG C/min in air, and keeping heating for 2 hours; and then, heating to 500 DEG C in the presence of inert atmosphere, keeping heating for 4 hours, and then, cooling to obtain the cobaltosic oxide-carbon porous nanofiber. The composite material has nano-scale size and high conductivity; when the cobaltosic oxide-carbon porous nanofiber is used as a negative electrode material of a lithium battery, the cycle life of the lithium battery can be prolonged, and the rate capability of the lithium battery can be improved; in addition, the preparation method is simple in process, good in repeatability, easy to implement and beneficial to industrial production.
Owner:JIANGSU UNIV OF SCI & TECH

Aspherical focal length-variable photoetching objective lens system

The invention provides an aspherical focal length-variable photoetching objective lens system, belonging to the technical field of optics, and aiming at solving the problem that the existing photoetching objective lens can not expose mask plate exposure patterns with different scale sizes by the same photoetching objective lens. The system provided by the invention from an object plane to an image plane sequentially comprises: the object plane, a first lens set, a second lens set, a third lens set, a fourth lens set, a fifth lens set and the image plane; the object plane is the plane where a mask plate is arranged; the first lens set is used for fixing the distance between the object plane and the first lens set of a focal length-variable system; the second lens set is used for changing the focal length of the photoetching objective lens and the size of the image plane; the third lens ser is used for compensating the movement of the image plane when a zooming group moves, so that the position of the image plane is invariably kept in the whole process of zooming; the fourth lens set has the negative power, the fifth lens set has the positive power, and a back fixed set consists of the fourth lens and the fifth lens, so that the invariable distance between the final lens of the photoetching objective lens which is near to one side of the image plane and the image plane can be guaranteed; and the image plane is the plane where an etching substrate is arranged.
Owner:CHANGCHUN INST OF OPTICS FINE MECHANICS & PHYSICS CHINESE ACAD OF SCI

Preparation method of graphene-oxide-based MoO2 high-performance electrode material of lithium/sodium ion battery

The invention discloses a preparation method of a graphene-oxide-based MoO2 high-performance electrode material of a lithium / sodium ion battery and belongs to the technical field of preparation of the electrode material of the lithium / sodium ion battery. The technical scheme is as follows: the preparation method comprises the following steps: dropwise adding (NH4)Mo7O24 aqueous solution into a graphene-oxide water dispersing system, fully stirring, dispersing to be uniform, and then evaporating water in the dispersing system to form molybdenum amino acid and derivative / graphene-oxide layered preform; then carrying out heat treatment on the molybdenum amino acid and derivative / graphene-oxide layered preform, and thus preparing the graphene-oxide-based MoO2 high-performance electrode material of the lithium / sodium ion battery. The preparation method disclosed by the invention has the advantages that the operation is simple, the reaction time is short, the repeatability is high, the cost is lower, the yield is large, and the structure of a product is easily controlled; and the prepared negative material of the battery reaches the nano-scale size, and has the characteristics of large specific capacity, good conductivity, low resistivity and high circulating times and the like.
Owner:SHAANXI UNIV OF SCI & TECH

Multi-illumination face recognition method based on morphologic quotient images

The invention provides a multi-illumination face recognition method based on morphologic quotient images. The method comprises the following steps that: illumination intensity of each position of a face is estimated, including light-source intensity and normal vector information; a quotient value of an original illumination image and an illumination estimation image is evaluated so as to obtain the reflectivity information of the face surface, namely face texture features unrelated to illumination conditions; because dividing operation can produce noise points which are unrelated to the face texture features in the prior shadow region, the original image is preprocessed before illumination estimation; illumination estimation adopts a morphologic closed operation method and uses the simplest and most convenient rectangular mean template; the scale size of the template adopts a dynamic principle; according to the characteristics of different local regions, the size of the template is chosen through self-adaptation; a quotient image result obtained through division is subjected to nearest neighbor classification; and a distance criterion adopts normalized correlation. The method has the advantage of improving the security of automatic face recognition systems, and has important application value in the field of biometrics recognition.
Owner:INST OF AUTOMATION CHINESE ACAD OF SCI
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