71results about How to "Complete detection" patented technology

The invention discloses a highly automated intelligent contract formal verification system and method. The method includes the following steps: step 001, converting an intelligent contract function requirement description document into an intelligent contract function requirement specification document described in a non-natural language, wherein the content of intelligent contract function requirement specification document includes target contract function specification description and security attribute description; step 002, creating a formal validation rule model library; step 003, automatically modeling the contract source code and / or bytecode by an automated modeling tool; step 004, parsing the abstract syntax tree generated in step 003, and allocating memory addresses for constantsand variables in the code; step 005, performing formal proof. The invention is adapted to program codes written in a plurality of high-level programming languages and also adapted to a plurality of formal languages, and simultaneously provides two automatic modeling modes of source code modeling and bytecode modeling, so that modeling can be carried out according to different modeling requirements of users, and the verification efficiency is further improved.

The present invention discloses an arithmetic of picking up magnetic resonance imaging cerebral active regions by sorting based on mode identification, which comprises the steps that cerebral active regions are extracted based on the multi-element mode distance between fine activity modes in partial cerebral regions for the pretreatment of an fMRI image; a partial consistent cerebral region is obtained by clustering; the combined activities of a plurality of tissues inside the partial consistent cerebral region are used for constructing the multi-element mode; the multi-element distance function is constructed by a mode sorting method to measure the separable characters of the partial cerebral region motion under different stimulation conditions, so as to judge whether the cerebral region is activated or not. The present invention indicates the cerebral motions under different stimulation conditions by multi-element mode information formed by multiple tissues inside the partial cerebral region directly, the multi-element mode can reflect the partial cerebral motion state over all, and multi-element statistical distance can be effectively integrated with the information in the partial cerebral region to measure the difference between different cerebral activate states, so the multi-element mode and the multi-element statistical distance ensure that the arithmetic of the present invention can detect the fine cerebral action mode more completely than the traditional fMRI analyzing technology.

The invention discloses a human body motion capture and posture analysis system and a method for carrying out foreground detection and automatic extraction of joint points through the human body motion capture and posture analysis system. According to the human body motion capture and posture analysis system, a human body motion image is captured through a camera, the human body foreground and the human body joint points are extracted from the human body motion image, and then human body motions are analyzed through a tracking and recognition algorithm. Due to the fact that a human body target in a video is extracted through an algorithm combining a background difference method and a frame difference method, the defect that background noise interference easily exists in the process of foreground detection based on the background difference method is overcome; due to the fact that the method with combination of a plurality of algorithms is adopted, automatic initialization of the human body joint points is achieved, and the defects that optical marks or manual marking is needed by a traditional joint point extraction method, and an existing automatic extraction method is large in calculated amount and poor in accuracy are overcome. The human body motion capture and posture analysis system is low in complexity and high in accuracy, and the intelligence and the efficiency of the human body motion capture and posture analysis system are improved.

The invention relates to a field of video tracking and discloses a binocular vision based dynamic object movement track identifying method. According to the invention, a binocular vision system includes two cameras mounted horizontally and in parallel and a computer connected to the cameras. The method includes steps of collecting image data of a dynamic object and transmitting the image data to the computer; using a background differencing method for processing the image data so as to acquire outline in each frame of the image data; acquiring two-dimensional coordinates of the dynamic objectthrough identification performed according to characteristics of the dynamic object; acquiring three-dimensional coordinates of the dynamic object through binocular vision matching calculation and a trigonometric survey principle; compiling movement track capturing software for acquiring the movement track of the object. Two-dimensional characteristic identification is performed on objects havinghigh movement speed, small boundary dimensions and unconspicuous two-dimensional shape characteristics by adopting an outline filtering algorithm and three-dimensional coordinate calculation is performed by utilizing results of binocular vision matching, so that movement track of the dynamic object is obtained and the detection and tracking efficiency is high.

The invention discloses an RGB-T image significance target detection method based on multi-level depth feature fusion, which mainly solves the problem that in the prior art, a saliency target cannot be completely and consistently detected in a complex and changeable scene. The implementation scheme comprises the following steps: 1, extracting rough multi-level features from an input image; 2, constructing an adjacent depth feature fusion module, and improving single-mode features; 3, constructing a multi-branch-group fusion module, and fusing the multi-mode characteristics; 4, obtaining a fusion output feature map; 5, training an algorithm network; 6, predicting a pixel-level saliency map of the RGB-T image. Supplementary information from different modal images can be effectively fused, image salient targets can be completely and consistently detected in a complex and changeable scene, and the method can be used for an image preprocessing process in computer vision.

The invention discloses a barrier detection method and a device thereof based on monocular vision. The method comprises the following steps: an image at the present moment is cut for obtaining a plurality of regions which are cut; self-vehicle motion parameters are obtained, and imaginary images at the present moment are generated by using all the pixels on the image t moment before the present moment after carrying out movement according to the self-vehicle motion parameters; the similarity level of the image and the imaginary image at the present moment is calculated, so as to determine candidate barrier area of the image at the present moment; whether each candidate barrier area has intersection with each cut area in the image at the present moment is calculated, if yes, the barrier judging percentage formed by the pixel area of intersection areas and the corresponding pixel area of the cutting areas is calculated, and barriers are determined according to the barrier judging percentage and preset barrier threshold. The method has the advantages of low faulty detecting ratio and complete barrier detection.

The invention discloses a test vector generating method for boundary scanning, which generates a test vector by integrating test information of a tested device. The method comprises the following specific steps: a, extracting the test information; and b, analyzing the test information, and generating a test vector matrix, wherein the vector matrix can be a single test vector matrix and a network interconnecting test vector matrix, and can perform self test and internet test of a single device respectively. The method generates the test vector by integrating the test information of the tested device; the method can conveniently generate the test vector and extract the information required by diagnosis; and simultaneously, the generated test vector is favorable for complete detection and more convenient operation.

The invention provides a boundary defect detection method and device and a detection device, wherein the method comprises the steps of obtaining an image to be detected; extracting the boundary regions of two adjacent image regions to obtain boundary region images; linearly expanding the boundary region image to form a linearly expanded image; detecting the boundary defects based on the straight-line image; when the boundary defects are detected in the straight line image, determining the position of the boundary defects in the image to be detected by using the boundary region image and the straight line image. By extending a first preset distance and a second preset distance respectively to two adjacent image regions in the normal direction of each pixel point on the boundary line, the first boundary connection line and the second boundary connection line are obtained, namely, all the boundary defects of the image to be detected are divided into boundary regions by setting appropriateboundary detection ranges, and the defects crossing the boundary regions can be completely detected after the boundary regions are straightly expanded, thereby improving the detection accuracy.

The invention discloses a complete and high-resolution test method for the motion characteristics of particles in turbid media. The method comprises the following steps of: performing continuous, complete and real-time detection on the motion characteristics of the particles in the process of 'solution-colloid-sediment' of the turbid media by combining the resolution relation between the characteristic value of a speckle pattern and particle motion and utilizing a dynamic speckle test light path according to the particle light scattering theory; simultaneously, performing transient study on different critical points of the particle motion in the process by using a femtosecond laser test light path to perform the analysis of high spatial and temporal resolution on transient dynamics of the particles in the turbid media; and combining the two steps to realize the complete test with high spatial and temporal resolution characteristic on motion images of the particles in the process of 'solution-colloid-sediment' of the turbid media finally. The method is a non-contact, high-accuracy and real-time online detection method, has the characteristics that the operation is simple and the application range is wide, and can be widely used in the fields of medical science, pharmacy, chemical production and the like.

The invention discloses an image significance object detection method based on multiscale low-rank decomposition and with sensitive structural information. The method comprises the following steps of: a three-dimensional volume data generation stage: performing superpixel decomposition and DT (Delaunay Triangulation) on a two-dimensional image to form three-dimensional volume data corresponding to the two-dimensional image, a Biharmonic distribution calculation stage: obtaining a Biharmonic diffusion result of each superpixel point, a subgeneration description stage: performing histogram statistics on L2 distances between sampling points on Biharmonic isolines of the superpixel points to form shape description of the isolines, and a multiscale low-rank decomposition stage: obtaining a final significance object detection result by differencing sparse matrixes obtained by the low-rank decomposition under different scales and performing residual error matrix summation based on the shape description of the Biharmonic isoline of each superpixel point. The method is based on GPU (Graphics Processing) parallel implementation, can detect one or more significance objects in the image, and has the characteristics of high detection precision of the significance object, complete detection of the significance object, good noise resistance and the like.

The invention discloses a fault detection method for scattered strands in power transmission line. The method comprises the following steps: A) obtaining an image of an image edge; B) detecting the obtained image for a first provisional line; C) keeping all pixel points that are of the same or close to colors of a power transmission line; D) carrying out the first filtering to the interference lines in the image of an image edge for a second provisional line, and the incomplete part in the second provisional line is supplemented for a third provisional line; conducting a second filtering to the interference lines in the image of an image edge so as to finally obtain all determined lines in the image of an image edge, or rather, determined power transmission lines as well as to determine the positions of the lines; E) determining the transmission directions and the widths of the power transmission lines; judging whether there exist hidden hazards for scattered strands and fault of scattered strands in a sliding window; and G) marking the number of areas on the power transmission line where scattered strands occur. According to the method provided by the invention, it is possible to locate the areas with scattered strands more accurately and robustly. And the decision errors can be reduced as well.

The invention discloses a geometric active contour model-based coastline detection method for a remote sensing image. The method comprises the following steps: (1) reading the remote sensing image comprising a to-be-detected coastline target by Matlab software, and filtering a target image by a median filter; (2) obtaining an initial contour of the coastline by a wavelet transformation method; (3) processing the coastline segmented in the step (2) by block tracking and local edge selection methods to obtain the initial contour of the coastline; and (4) with the obtained initial contour of the coastline as an improved input of a geometric active contour model, and finally obtaining the accurate and continuous coastline through processing of the geometric active contour model. According to the geometric active contour model-based coastline detection method for the remote sensing image, the influence on coastline detection caused by noise can be effectively suppressed; and accurate, continuous and complete coastline detection is achieved.

The invention discloses an unconstrained in-video salient object detection method combined with an objectness degree. The unconstrained in-video salient object detection method specifically comprises the steps of: (1) inputting an original video sequence F={F<1>, F<2>, ..., F<M>}, wherein a t-th frame in the sequence is referred to as F<t>; (2) adopting a video saliency model and an objectiveness object detection algorithm for the video frame F<t>, so as to obtain an initial rectangular region for salient object detection; (3) updating an objectness degree probability graph and an object probability graph through iteration for the video frame F<t>, and adjusting the size of the rectangular region for salient object detection continuously, so as to obtain a single-frame salient object detection result; (4) and utilizing a dense optical flow method algorithm to obtain a motion vector field of pixel points of the video frame F<t>, and calculating the overlapping degree of the rectangular regions for salient object detection of the adjacent frames, so as to obtain a final salient object detection result. The unconstrained in-video salient object detection method updates the objectness degree probability graph and the object probability graph through iteration, enhances the precision of spatial domain salient object detection results, improves time consistency through sequence-level refining, and can detect salient objects in a video more accurately and completely.

The invention discloses a robust sparse representation and Laplace regular term-based salient object detection method, and mainly aims at solving the problem that the existing method cannot completely and consistently detect the salient objects in complicated images. The method comprises the following steps of: 1, segmenting an input image to obtain a superpixel set; 2, constructing a background dictionary by adoption of superpixels at a boundary region; 3, respectively restraining the consistency between representation coefficients and reconstruction errors in a robust sparse representation model by adoption of two Laplace regular terms, and obtaining a representation coefficient matrix and a reconstruction error matrix by utilizing a background dictionary solution model; 5, constructing salient factors by combining the representation coefficient matrix and the reconstruction error matrix, so as to obtain a superpixel-level saliency map; and 6, mapping the superpixel-level saliency map to obtain a pixel-level saliency map. Experiments indicate that the method has relatively good background suppression effect, is capable of completely detecting salient objects of images, and can be used for the salient object detection of complicated scene images.

The embodiment of the invention discloses a method and a device for tracking a fault, and relates to the technical field of processors. By the method and the device, faults inside a simulation full system can be tracked, and fault behaviors can be detected and recorded in detail. The method comprises the following steps of: when the fault is injected, detecting a running state of the current equipment; when the current equipment has the fault, determining the current equipment to be a fault source, and identifying the current equipment by using identification information of the fault source; determining fault data transmitted by the fault source to be a fault carrier, and identifying the fault data by using identification information of the fault carrier; and generating fault path information according to the fault source and the fault carrier, and recording fault injection information. The embodiment of the invention is mainly applied to a fault tracking process.

The present invention discloses a graph based unconstrained in-video significant object detection method. The method specifically comprises the following steps: (1) inputting an original video frame sequence, wherein a t-th frame is referred to as Ft as shown in the specification; (2) dividing an entire video frame into super-pixel regions; (3) obtaining a motion vector field of pixel points in the formula as shown in the specification by using a dense optical flow algorithm, and separately extracting a super-pixel region class and a global motion histogram; and (4) constructing an undirected weighted graph as shown in the specification, separately calculating a shortest path from the super-pixel region to virtual background nodes as shown in the specification, and performing accumulation on weights of edges between the nodes in the path and taking the weights as a motion significant value of the super-pixel region to generate a motion significant graph of a current frame, and according to the binarized significant graph, re-estimating a motion histogram of a background and updating the significant value of the super-pixel region. According to the graph based unconstrained in-video significant object detection method provided by the present invention, the method is based on a graph,and saliency detection is performed by performing iterative estimation on motion of the background, so that significant objects in the video can be detected more accurately and completely.

The invention relates to a UAV line patrol flight control system and method. The system comprises a UAV, a data collection assembly and a controller; the data collection assembly collects flight related data and transmits the data to the controller, and the controller comprises a UAV driving node and a line patrol flight control node; and the controller receives video image data of the UAV, a UAVdriving node processes the video image data and then sends the video image data to a line patrol flight control node, the line patrol flight control node processes the video image data and identifiesa track, a track error is detected then, the left-right linear velocity and deflection angle speed are obtained after PI control, the left-right linear velocity and deflection angle speed together with the forward linear velocity is sent to the UAV driving node, the UAV driving node processes the velocities and speed and then sends processed data to the UAV, so that the UAV carries out line patrolflight. In the method, the system is used to carry out line patrol flight, PI control is carried out on the flight error, the flight track of the UAV approaches a preset track, autonomous flight following the preset track can be realized, and patrol line flight of the UAV is realized.

The invention provides a refrigerator. The refrigerator comprises a refrigerator body and a door. A hyperspectral detection system used for detecting nutrient ingredients of food is arranged in the refrigerator body. The hyperspectral detection system comprises a hyperspectral device, a food carrying platform and a control and analysis module. The detection end of the hyperspectral device comprises a light source used for sending out light signals and a sensing device used for receiving reflecting signals, the food carrying platform is located above the detection end of the hyperspectral device, and the control and analysis module is connected with the light source and the sensing device and used for controlling the light source and analyzing the reflecting signals. In the detection process, the light source sends out the light signals to detected food, the sensing device receives the reflecting signals of the detected food, the reflecting signals are analyzed through the control and analysis module, and the nutrient ingredients of the detected food are obtained. Meanwhile, the whole detection system is small in size and occupied space and very convenient to use; maintenance is easy, a user can operate the refrigerator by himself / herself, and the use experience of the user is improved.

The invention provides a refrigerator. The refrigerator comprises a refrigerator body and a door. A hyperspectral detection system used for detecting nutrient ingredients of food is arranged in the refrigerator body. The hyperspectral detection system comprises a hyperspectral device, a food carrying platform and a control and analysis module. The detection end of the hyperspectral device comprises a light source used for sending out light signals and a sensing device used for receiving food reflecting signals, the food carrying platform is located above the detection end of the hyperspectral device, and the control and analysis module is connected with the light source and the sensing device and used for controlling the light source and analyzing the food reflecting signals. A switch module used for controlling the hyperspectral device is further arranged. In the detection process, the switch assembly controls the light source to send out the light signals to detected food and then controls the sensing device to receive the food reflecting signals of the detected food, the food reflecting signals are analyzed through the control and analysis module, then the nutrient ingredients of the detected food are obtained, the hyperspectral device does not need to be taken out of a hyperspectral containing device, operation is very convenient, and the use experience of a user is improved.

This invention discloses a test method for electronic disks including: initializing a disk to set it in a processor system, then carrying out register test and formating it to prepare for reading and writing data, initializing specific test data then testing its data line, testing its open-circuit and short-circuit of the address line, formatting the disk to clear out the test data and quiting from the flow to finish the test.

The invention relates to the field of power system simulation training, and especially relates to a GIS supersonic partial discharge live-line detection simulation implementation method. The method comprises the following steps: (1), collecting a supersonic partial discharge typical defect map and feature data; (2), analyzing the envelope features of a simulation map; (3), simulating and generating a discharge spectrum map: generating a test spectrum on a simulation tester through analyzing various types of defect spectrums and giving consideration to the time domain and frequency domain features; (4), simulating the consistency of the hardware equipment for partial discharge detection with the reality; (5), simulating the consistency of the whole testing process with an actual process. The method achieves a typical defect signal of supersonic partial discharge via the principle of statistics through the digital simulation, achieves the manual control, and brings convenience to a learner for mastering the defect position and judging the type of a defect.

The invention provides a self-adaptation ionic current detecting device which comprises an ignition isolation module, a high-voltage isolation module, a bias power supply module, a voltage dividing module, a signal preprocessing module and a variable resistance module. One end of the ignition isolation module is connected with an ignition module of an engine, and the other end of the ignition isolation module is connected with the input end of an ignition plug of the engine and is connected with one end of the high-voltage isolation module. The other end of the high-voltage isolation module is connected with a positive electrode of the bias power supply module. A negative electrode of the bias power supply module is connected with one end of the voltage dividing module. The other end of the voltage dividing module is connected with the output end of the variable resistance module which is provided with an engine combustion mode control signal receiving end, and the output end of the voltage dividing module is connected with the input end of the signal preprocessing module. The self-adaptation ionic current detecting device can carry out effective isolation on interference from ignition, high voltage, a background and the like, self adaptation on engine SI / SCCI mode switching is achieved, and the ionic current of an internal combustion engine cylinder inner combustion state can be detected accurately and completely in real time.

The invention discloses a method for detecting output power of dual-mode RRU (remote radio unit) equipment. The method comprises the following steps of according to the cell carrier wave and power conditions of the existing TD-SCDMA (time division-synchronization code division multiple access) and TD-LTE (time division-long term evolution), selecting a dual-mode training sequence in the output power detection mode, wherein the dual-mode training sequence is the same as the cell frequency feature; in the next preset cycle, according to the TD-LET time slot, sending the dual-mode training sequence in the coincided time period of TD-SCDMA guard period and TD-LTE guard period in the time axis, sampling data, calculating the accumulative sum, and repeating the step for multiple times; converting the accumulative sum obtained by multiple data sampling into the power value, and calculating the feedback average power; and according to the feedback average power, calculating the output power. The method has the advantage that when the RRU equipment simultaneously works in the TD-SCDMA mode and the TD-LTE mode, the output power can be accurately and completely detected.

The invention discloses a machine vision selection device and method for infertile eggs in whole tray of hatching duck eggs. The machine vision selection device comprises a crawler-type conveying belt, the crawler-type conveying belt is in driven connection with a first motor, a light chamber is arranged in the center of the crawler-type conveying belt, an egg tray is placed on the crawler-type conveying belt, a plurality of hatching duck eggs hatched for 5 days are vertically placed in the egg tray, a whole tray egg suction device is arranged in the light chamber, the whole tray egg suction device is connected to the inner side wall of the light chamber in a sliding mode, an image acquisition mechanism is arranged on the light chamber, the image acquisition mechanism is in signal connection with a control mechanism, the image acquisition mechanism is used for acquiring images of the whole tray of hatching duck eggs and identifying infertile eggs and fertile eggs for the hatching duckeggs, the control mechanism is used for controlling the whole tray egg suction device to move and grab hatching duck eggs belonging to the infertile eggs, and the whole tray egg suction device is usedfor grabbing the hatching duck eggs on the egg tray. According to the machine vision selection device and method, the problem that the automation degree and the intelligent degree of the poultry egghatching industry are low can be practically solved.

The invention relates to a video object cooperative segmentation method based on a track directed graph. The method comprises the following steps of (1), inputting each frame sequence Fm, t(t=1, ..., Nm) of a video set; (2), generating a motion vector field, an initial significance graph and a candidate object for the video frame Fm, t; (3), performing frontward and backward tracking on each candidate object, and performing maximal inhibition and track segmentation, thereby forming a track set; (4), constructing a directional weighted graph G=(V,E), wherein the track is a directional edge which is established between nodes in the graph according to a matching score; (5), converting the directional weighted graph G=(V,E) to a non-directional weighted graph G=(V,E'), extracting a maximal clique by means of a maximal clique extracting algorithm, calculating the weighted clique score of each maximal clique, using a track area which corresponds with the clique with highest weighted clique score as a main object area, performing a popular sequencing algorithm for generating an objective significance graph, and obtaining a final segmentation result by means of GrabCut; and (6), according to the obtained object segmentation result, updating an initial significance graph, calculating the maximal clique score and obtaining the object of other kind.

The invention discloses an automatic pipe fitting test center. The invention belongs to the technical field of automation equipment, the test center comprises a pipe fitting testing device, a robot, at least two trays and a control cabinet, the pipe fitting testing device is used for detecting a pipe fitting. The robot and the pipe fitting testing device are arranged adjacently. At least two traysare arranged, and the trays are adjacent to the robot. At least one tray is used for placing a pipe fitting to be detected; at least one tray is used for placing a detected pipe fitting, the controlcabinet is electrically connected with the pipe fitting testing device and the robot, the robot is used for clamping the pipe fitting to be detected on the tray onto the pipe fitting testing device, and the robot is further used for taking down the pipe fitting detected by the pipe fitting testing device and placing the pipe fitting onto the tray. The test center is convenient to use, feeding, discharging and testing are automatically completed, the detection efficiency is greatly improved, the labor intensity is reduced, detection is complete, the detection accuracy is well guaranteed, and the labor cost is reduced.

The invention discloses a method for detecting non-metallic inclusions in steel, which adopts the following process: carrying out solid solution strengthening on a sample to be detected to prepare a rod-shaped sample; taking a stainless steel sleeve as a cathode, taking a rod-shaped sample as an anode, and performing electrolytic extraction by adopting a semipermeable membrane for isolation, wherein the electrolyte comprises the following components in proportion: 0.01 g / ml-0. 1 g / ml of tetramethylammonium chloride, 0.1 ml / ml-0. 25 ml / ml of acetylacetone and the balance of a methanol solution with the mass fraction of 95%-99%; dissolving the electrolyzed electrolyte in absolute ethyl alcohol, and filtering by using a filter membrane; dissolving the inclusions on the filter membrane in the absolute ethyl alcohol solution again, and obtaining clean inclusions after magnetic separation and filtering; inlaying clean inclusions on a metal plane with a conductive adhesive, and spraying platinum to prepare an ASPEX observation sample; and detecting the ASPEX observation sample by using an ASPEX automatic scanning electron microscope to obtain detection information of all extracted inclusions. According to the method, the non-metallic inclusions in the steel can be nondestructively, completely and quickly detected, and the manual workload and errors caused by manual work are greatly reduced.

The invention relates to a detection device and a detection method for a control circuit of a smoke bomb launcher barrel. The detection device can be directly inserted into the launcher barrel withoutbeing detachably connected to any part of the circuit when detecting the control circuit of the smoke bomb launcher barrel, so that the detection device has the characteristic of quick, accurate, correct and intact detection. An existing detection device for the control circuit of the launcher barrel of a smoke screen casting device detects the condition of the launching control circuit in a position of an external plug of the launcher barrel, and avoids the condition of on-off of a power supply of an inner structure of the launcher barrel, so that whether the control circuit in and out of the launcher barrel meets the smoke bomb launching and controlling condition or not completely cannot be detected truly.