2710 results about "Self adaptation" patented technology

The invention belongs to the technical field of GNSS engineering safety, and relates to an anti-interference normal null widening method for a dynamic GNSS receiver. On the basis of a normal-distribution probability model, a null widening model of the dynamic GNSS receiver is provided. The model is linearized on the situation of small angle changing, and frequency spectrum gains formed before and after widening are compared by means of a Matlab simulation tool. By means of a power insertion algorithm, the relationship of an output SNR and an input INR is analyzed before and after widening, and ultimately engineering realizability of an algorithm is put forward through FPGA and DSP platform verification. As is shown by a result, widening null formed through the widening algorithm can effectively counteract interference sources changing within a certain range, the aim of preventing dynamic interferences is achieved and robustness of a self-adaptation algorithm is improved.

The invention discloses a binocular vision navigation system based on an inspection robot in a transformer substation. The binocular vision navigation system comprises a robot body, an image collecting system, a network transmission system, a vision analysis system, a route planning system and a robot control system, wherein the image collecting system is arranged in front of the robot body and used for collecting environmental information images of a forwarding road and then uploads the environmental information images to the vision analysis system on the basis of the network transmission system; the vision analysis system detects a barrier in the road area of the transformer substation according to binocular image information collected by the image collecting system and inside and outside parameters of a camera; the route planning system plans a route according to environment information detected by the vision system and timely adjusts the walking routes of the robot, so that the robot can be prevented from being collided with the barrier; and the robot control system controls moving of the robot body according to the route planned by the route planning system. The invention simultaneously discloses a vision navigation method. By means of the system and the method, the self-adaptation ability of the robot to the environment is improved, the autonomous navigation function of the electric robot is really realized in the complicated outdoor environment, and the flexibility and the safety of the robot are improved.

The invention provides a human body behavior identification method based on an inertial sensor. The method comprises the steps of acquiring human body behavior data of testees by means of the inertial sensor, conducting sliding window segmentation on the acquired human body behavior data, conducting feature extraction on a triaxial accelerated speed subsequence and a triaxial angular speed subsequence which are obtained after sliding window segmentation, conducting feature fusion on a feature vector to form a sample set of the human body behaviors of the testees, conducting feature selection on the sample set by means of the least correlated maximum redundant algorithm and the Bayes regularization sparse polynomial logistic regression algorithm, obtaining the classification feature vectors of all human body behaviors of all the testees, obtaining a classifier model of each human body behavior by means of a fuzzy least square support vector machine, and obtaining a human body behavior identification result after the human body behavior data are tested by means of the fuzzy least square support vector machine. By means of the human body behavior identification method, self-adaptation and identification efficiency can be improved.

Provided is a quick detecting method for moving objects in a dynamic scene. The quick detecting method for the moving objects in the dynamic scene comprises carrying out sequence interframe registration on moving images by utilizing CenSurE feature points and a homography transformation model, obtaining a registering frame of a former frame taking a current frame as reference, carrying out subtraction on the registering frame with the current frame to obtain a frame difference image to generate a foreground mask, building a dynamic background updated in real time according to space distribution information of the foreground mask in the current frame, obtaining a background subtraction image based on a background subtraction method, carrying out statistics on the probability density of the gray level of each pixel in the frame difference image, when the sum of the probability density of the gray level of a pixel is larger than 2phi(k)-1, taking the gray level as a self-adaptation threshold value, judging pixels with values of gray levels larger than the threshold value as foreground pixels, and otherwise judging the pixels as background pixels. The quick detecting method for the moving objects in the dynamic scene can reach the processing speed of 15frame/s and can obtain relatively integral moving objects under the premise that the detecting speed is ensured, and therefore, index requirements such as rapidity, noise immunity, illumination adaptation, target integrity and the like of the detection of the moving objects in the dynamic scene can be met.

The invention discloses a self-adaptation three-dimensional attitude positioning method based on microinertia and a geomagnetic technology, comprising the following steps of: (1) inducing a motion attitude of a carrier by utilizing a sensor of a microinertia measuring device; (2) setting an initial attitude of the microinertia measuring device and accelerated speed and geomagnetic field information under a global coordinate system; (3) solving the attitude value of the microinertia measuring device; (4) predicting the attitude value of the microinertia measuring device by utilizing triaxial micro-gyroscope sensor data; (5) carrying out confidence judgment on the triaxial micro-acceleration sensor data and triaxial magnetic field sensor data, detecting the interference of a surrounding environment, and setting self-adaptation parameters; (6) obtaining the attitude error value of the microinertia measuring device by utilizing the triaxial micro-acceleration sensor data and the triaxial magnetic field sensor data which are processed in the step (5); (7) fusing the attitude predictive value obtained from the step (4), corrected information obtained from the step (6) and the self-adaptation parameters obtained from the step (5) to obtain the attitude value of the microinertia measuring device; and (8) outputting attitude information.

The invention belongs to the technical field of robots, and relates to a crawler-type pipeline robot with active adaptation and self-adaptation functions. A series combination mechanism, which is composed of a screw mechanism, a rocker slider mechanism and a parallelogram mechanism and driven by a stepper motor, is utilized to actively adapt to different pipeline diameters and keep a certain pressure between the crawler and pipeline inner wall. A set of butterfly springs arranged between the screw nut and slider are stressed to generate deformation to automatically adapt to the working conditions of pipeline joints, uneven-surface partial obstacles and the like in the pipeline. Three servomotors drive three sets of synchronous belt wheels to rotate through the transmission of a bevel gear and a cylindrical gear, thereby driving the three bifacial synchronous notched belts used as pipeline robot crawlers to move, and implementing the running function of the pipeline robot. The crawler-type pipeline robot has the simple structure, high operation and running safety, high environmental adaptability and wide application range, is convenient to use, and can work in pipelines with different diameters.

The invention discloses a point cloud partition method and device, and relates to the technical field of three-dimensional reconstruction. The point cloud partition method includes the following steps: (S1) line-by-line scanning is carried out on a measured scene through a depth transducer to obtain depth information of the measured scene, and coordinate transformation is carried out on the depth information of the measured scene to obtain three-dimensional information of the measured scene under a local coordinate system; (S2) ground point clouds are partitioned from the three-dimensional information; (S3) clustering partition is carried out on non-ground point clouds in the self-adaptation threshold value clustering partition mode, wherein the non-ground point clouds are the other point clouds except the ground point clouds in the three-dimensional information. By means of the point cloud partition method and device, clustering partition is carried out on the non-ground point clouds in the self-adaptation threshold value clustering partition mode, and insufficient partition and excessive partition caused by clustering partition on the non-ground point clouds with fixed threshold values are effectively avoided.

The invention provides a face detecting and tracking method and a device. The method comprises the steps of inputting a face image or a face video, preprocessing the face image or the face video in an illumination mode, detecting a face by usage of an Ada Boost algorithm, confirming an initial position of the face, and tracking the face by the usage of a Mean Shift algorithm. According to the face detecting and tracking method and the device, a self-adaptation local contrast enhancement method is provided to enhance image detail information in the period of image preprocessing, in order to increase robustness under different illumination conditions, face front samples under different illumination are added to training samples and accuracy of the face detection is increased by adoption of the Ada Boost algorithm in the period of face detection, in order to overcome the defect that using color of the Mean Shift algorithm is single, grads features and local binary pattern length between perpendiculars (LBP) vein features are integrated by adoption of the Mean Shift tracking algorithm in the period of face tracking, wherein the LBP vein features further considers using LBP local variance for expressing change of image contrast information, and accuracy of the face detection and the face tracking is improved.

The invention relates to a modularizing self-adaptation adjusting clamp used in the automobile welding, comprising a based station(1), a clamping mechanism(100) fixed on the based station(1) , an independent principal positioning mechanism(200), and an online real-time detecting and self-adaptation adjusting device(300). The online real-time detecting and self-adaptation adjusting device (300) is composed of a stepping motor (305), a slider (308), a straight-line guiding rail (309) and a transmission device. The transmission device, which is driven by the stepping motor (305), pushes a pushing rod (301) and the slider to slide along the straight-line guiding rail (309). The invention is more flexible and is convenient for the connection and combination of every component by means of modularized, standardized and serialized clamp elements, can be adapted to various work within a certain range and type, can effectively reduce the manufacturing cost, effectively controls the precision of the key points of workpieces and improves the welding quality and efficiency.

The invention discloses a self-adaptation control method and device of an air conditioner. The method includes setting a mapping relation of outdoor environment temperature and a heating threshold and a refrigeration threshold in advance; obtaining indoor environment temperature and the outdoor environment temperature after the air conditioner is in a self-adaptation operation mode and an environment temperature obtaining command is detected; obtaining the heating threshold and the refrigeration threshold which correspond to the obtained outdoor environment temperature according to the preset mapping relation of the outdoor environment temperature and the heating threshold and the refrigeration threshold; calculating the difference of the indoor environment temperature and target temperature set by a user, comparing the difference with the heating threshold and the refrigeration threshold, confirming a practical operation mode of the air conditioner according to a comparison result, and controlling the air conditioner to operate in the practical operation mode. The method and device enables operation modes of the air conditioner to be dynamically adjusted according to changes of the indoor environment temperature, can accelerates temperature adjustment, and achieves the aim of energy saving and electricity saving.

The invention provides a voltage self-adaptation wireless charging device and method which comprises a receiving end device and an emitting end device. The voltage self adaptation wireless charging device is characterized in that the receiving end device comprises a wireless charging receiving unit, a receiving coil and a charged device, the emitting end device comprises a wireless charging emitting unit and an emitting coil. Another technical scheme of the voltage self-adaptation wireless charging device and method provides a voltage self-adaptation wireless charging method adopting the voltage self-adaptation wireless charging device. The voltage self-adaptation wireless charging device and method has the advantages of charging a plurality of battery cells and the wireless charging device of various voltage levels by using a wireless charging emitting device, thereby enabling users unnecessarily to consider the problem of whether the charging voltage level and the emitting circuit are matched or not, thereby greatly widening the range of application of the wireless charging emitting unit.

Disclosed is an operation cross-domain control method in a grid computing environment; the method uses a trust mechanism to realize the operation control in the grid environment, makes trust evaluation to the available resources in the grid, analyzes the operation to be processed, and uses a mobile agent to transfer the operation to an appropriate resource for execution, according to the information provided by a control system in the grid. The proposal overcomes the disadvantages of poor reliability, non-guaranteed response time, over-long running time on the resources and imperfect fault management in other operation control proposals, and can achieve self-adaptation to the resource and operation control in the grid, reduce the grid traffic and improve the utilization rate of the network to form an operational parallel solution, thereby achieving the purpose of improving the utilization efficiency of grid resources and the execution efficiency of grid computing, speeding up the execution of tasks and improving the accuracy of the results so as to enhance the processing efficiency of distributed systems.

The invention discloses a self-adaptive configuration method and device for a mobile phone terminal, aiming at solving the technical problem that network parameters are required to be manually reconfigured when the terminal is switched among different operators. By acquiring the network parameters, recognizing the network operators and then acquiring operating parameters related to the network operators, the operating parameters such as emergency call numbers, short message centers, access point names, homepages and the like of the mobile phone terminal can be self-adaptively configured, the network self-adaptation of the mobile phone terminal can be realized, convenience is provided for users and the situation that certain functions of the terminal are unavailable because of setting errors can be avoided when the users roam among different countries, regions or operators.

The invention discloses a multi-behavior combining auto-navigation method for a mobile robot in an unknown environment. The method is characterized in that the method comprises the following steps that (1) current azimuth angle is obtained in real time according to the relative positions of an object and the mobile robot, and a plurality of distance parameters are obtained in real time according to the status of obstacles around the mobile robot; (2) a multi-output support vector machine fuzzy controller outputs a corner value Theta i and a velocity value vi according to the obtained azimuth angle and distance parameters, wherein i is equal to 1, 2 or 3; (3) a multi-output support vector machine environmental-identification controller inputs signals and outputs weight parameters wi of three subbehaviors according to input signals of the azimuth angle and the distance parameters, wherein i is equal to 1, 2 or 3; and (4) current corner value Theta i and velocity value vi of the mobile robot used for navigation are calculated according to the formula. The multi-behavior combining auto-navigation method adopts intelligent control strategy, and has the advantages of strong self adaptation, high navigation reliability and excellent effect.

The invention discloses a page layout self-adaptation method and device. Program codes for adjusting page layout are inserted; based on the inserted program codes, the screen display width of a mobile terminal is obtained; according to the obtained screen display width, the page display width of a webpage is adjusted based on the preset proportion; according to the page display width, the display widths of elements which form the page of the webpage are adjusted, meanwhile, the display positions of the elements are arranged to be in a floating display mode; the webpage does not need to be designed and modified again, the page of the webpage can be automatically adaptive to a mobile terminal display screen, the display effect of the original webpage is not affected at all, the man-machine interactivity is improved; in addition, self adaptation can be achieved only by inserting corresponding JS codes, a large amount of manpower is saved, meanwhile, if corresponding visual effect needs to be changed, only corresponding codes need to be changed, cost is low, and later-period maintaining and upgrading are convenient.

Provided are a stroke patient rehabilitation training system and method based on brain myoelectricity and a virtual scene. Control over the virtual rehabilitation scene is achieved through myoelectric signals, and rehabilitation training intensity is adjusted in a self-adaptation mode with a brain myoelectricity fatigue index combined. The design of the virtual rehabilitation scene is completed with the needs of stroke patient rehabilitation training and the advice of a rehabilitation physician combined, the brain fatigue index is provided, and quantitative evaluation on brain region fatigue is achieved. The surface myoelectric signal features under different motion modes of an arm are extracted, the motion intention of a patient is obtained, and control over the virtual rehabilitation scene is achieved. The muscle fatigue and brain fatigue index comprehensive features are extracted, the fatigue state of a rehabilitation patient is obtained, self-adaptation rehabilitation training scene adjusting is achieved, rehabilitation training intensity is relieved or enhanced, and secondary damage caused by improper training is avoided. The system and method have the advantages of being high in safety, high in intelligence and scientific in training, and damage cannot happen easily.

The invention discloses a printing image defect detection method, which comprises the following steps: carrying out real-time image model learning in a gray scale region and a gradient region by aiming at specific images on a large image; comparing the large image to be detected to the a gray scale region model and a gradient region model established during learning; realizing small-dimension strong-contrast defect detection through Blob cluster analysis; when no small-dimension strong-contrast defect is detected, dividing the large image into sub regions, and respectively calculating image integrated features; adopting a variable threshold method for carrying out threshold division on each sub region; carrying out Blob cluster analysis on divided images; and realizing the large-area weak-contrast defect detection. Compared with the prior art, the invention does not rely on a reference template, is not sensitive on the real-time imaging brightness change, can overcome the defects of missing detection, error detection and poor self adaptation of the template by using a reference template comparison detection method, and can simultaneously solve the problems of eliminating tiny wrinkles and blackspots in printing and papermaking industries.

The invention belongs to the field of agricultural robots and discloses an under-actuated three-finger type fruit-vegetable flexible picking device which comprises under-actuated finger mechanisms I, a tilting driving mechanism II, a telescopic driving mechanism III, a pruning mechanism IV, a palm supporting disc, a finger driving motor connecting disc and a wrist connecting clamping disc. The three sets of under-actuated finger mechanisms I are evenly distributed on the palm supporting disc which is fixedly connected with the finger driving motor connecting disc. The finger driving motor connecting disc is fixedly connected with the wrist connecting clamping disc which is connected with a wrist of a robotic arm. The tilting driving mechanism II and the telescopic driving mechanism III drive the pruning mechanism IV to tilt and stretch, and the pruning mechanism achieves shear movement. The using range and the using flexibility are expanded, the high picking success rate can be achieved, and mechanical damage on plants and fruits is lowered. Picking of sphere-similar fruits or clustering-growing fruits can be achieved. The device has the advantages that fruit shape self-adaptation capacity is achieved, and contact force is controllable.

The invention discloses a breakdown intelligent classification and positioning method of an electric transmission line. The technical scheme of the breakdown intelligent classification and positioning method of the electric transmission line breakdown is that the advantages of three kinds of technologies of a support vector machine (SVM), self-adaptation nerve fuzzy inferences (SVM) and radial based function (RBF) neural networks are concentrated. The breakdown classifiers and positioners of the SVM, the SVM and the RBF neural networks are designed. Positioning errors, classification accuracy and model operation time are used as evaluation indicators. According to the standard that accuracy is preferred and efficiency is taken into account, intelligent selection of an optimal classifier and an optimal positioner is achieved under different breakdown conditions, and optimal breakdown classification and positioning effect is achieved. Meanwhile breakdown serious extent and repair indicators are designed to evaluate breakdown injury extent and breakdown repair difficulty. The breakdown intelligent classification and positioning method of the electric transmission line effectively improves power supply reliability, reduces outage cost, and meanwhile greatly reduces workload of maintenance personnel and improves working efficiency.

The invention discloses a multi-strategy and multi-object self-adaptation traffic control method. The multi-strategy and multi-object self-adaptation traffic control method is characterized by comprising the following steps that (1), traffic state inspection equipment collects traffic flow data in real time, obtains the traffic state information and sends the traffic state information to an intersection signal machine, and the intersection signal machine uploads the traffic flow data to a signal center control system; (2), the intersection signal machine calculates the traffic intensity of an intersection, judges the traffic states of the intersection and adjusts the control plan of the current intersection in real time according to the different traffic states; (3), according to the intersection traffic flow data uploaded in real time, the signal center control system optimizes the intersection signal control scheme from the global angle of a road network. According to the control method, the road network grade global optimization self-adaptation signal control method is provided through judgment and analysis of the traffic states of the road network, and artery coordination control applicable to any traffic state is achieved.

The invention discloses a multi-channel self-adaptation frequency-hopping processing method suitable for an aeronautical ad-hoc network. The method comprises the steps of (1) evaluating all frequency points; (2) dividing a frequency-hopping full band into m physical channels, and initially allocating n frequency points to each physical channel to form frequency sets; (3) selecting frequency points with the lowest power in other physical channels to replace interfered frequency points in the selected physical channel; (4) dynamically selecting the current idlest physical channel according to the idleness degree of the current physical channels when information to be sent of a node MAC layer is sent; (5) carrying out modulation and frequency-hopping transmission on framed data through the selected physical channels. The invention further discloses a multi-channel self-adaptation frequency-hopping processing system suitable for the aeronautical ad-hoc network. The system comprises a spectrum sensing module, a self-adaptation frequency point allocating module and channel idleness degree statistics module. According to the multi-channel self-adaptation frequency-hopping processing method and system suitable for the aeronautical ad-hoc network, the network reliability is improved through spectrum sensing processing and design of self-adaptation frequency point allocation protocols.

The invention discloses a self-adaptation voice detection method based on multi-domain joint estimation. Voice can be detected in a complex environment in a self-adaptation mode, accuracy and reliability are good, and the complexity of detection is low. The voice detection method includes the steps of signal preprocessing, frequency spectrum stability judging, stable noise voice detecting, non-stable noise voice detecting and voice segment signal outputting.

The invention discloses a self-adaptation camouflage design method based on a background. According to the method, on the basis of a texture synthesis algorithm based on pixels, a whole camouflage image is synthesized pixel by pixel; in the synthesis process, a self coding algorithm is adopted to extract a texture primitive containing background sample complete texture features, and a neighborhood of the pixels to be synthesized is determined; by means of the neighboring correlation principle and a helix supplement search mechanism, a neighborhood matched with a target pixel neighborhood is searched for in a background sample; similarity matching is carried out through a pixel and texture similarity matching rule, pixels in the neighborhoods which are most similar in the sample are selected to serve as synthesis pixels, pixels of a target image are synthesized according to the sequence of scanning lines, and after synthesis, color quantization is carried out on the image by means of the clustering algorithm to generate a final camouflage image. By means of the method, similar camouflage images can be synthesized quickly according to the background, and the application aim of quickly camouflaging a target in a self-adaptation mode can be achieved.