337results about How to "Reduce detection impact" patented technology

The invention discloses a method for highly sensitive quantitative detection of quantum dot fluorescence immunochromatographic assay. The method includes: building a fluorescence immunochromatographic assay test strip on the basis of optimizing the structure of the test strip and components by the aid of excellent fluorescent characteristics of quantum dots and by means of combining quantum dot fluorescence labeling technology and immunochromatographic assay; detecting fluorescence signal strength of a quantitative belt and a quality control belt by the aid of a fluorescence quantometer and correcting the fluorescence strength of the quantitative belt by the aid of the quality control belt after immunochromatographic assay of the test strip; and further quantitatively detecting analyte according to a standard curve obtained by the fluorescence quantometer. The method is simple, rapid, accurate, low in cost and quite high in sensitivity. Compared with a conventional colloidal gold immunochromatographic assay method, the method has the advantages of fine labeling stability, low non-specificity, high sensitivity, wide linear range and accuracy in quantization. The method is applicable to samples such as blood samples, urine samples, spittle, excrement and the like, and can be applied to detection of critical illness, poison, food safety and the like.

The invention discloses a quantum dot multicolor marking method for quantitative detection of various cardiovascular disease markers and a kit of troponin I/creatine kinase isoenzyme/myohemoglobin. The method realizes fluorescent quantitative detection by utilizing excellent fluorescent properties of quantum dots and combining a multicolour marking technology and an immunochromatographic assay on the basis of optimizing each component of a test strip. Compared with the common collaurum immunochromatographic assay, the method has the advantages of good mark stability, low nonspecificity, high sensitivity, wide linear range, small cross interference, and accuracy in quantification. The kit disclosed by the invention is used for carrying out quantification detection on the troponin I, the creatine kinase isoenzyme and the myohemoglobin simultaneously, is suitable for detection of whole blood, blood serum and plasma samples, can provide a reference for cardiovascular and cerebrovascular disease diagnosis, and is widely applied to primary hospitals and clinics.

The invention discloses an automatic LCD screen detecting cabinet based on machine vision. The automatic LCD screen detecting cabinet based on machine vision comprises a cabinet base, a darkroom, a multi-station rotating plate system, an automatic machine vision adjusting system and an automatic PG screen pointing system. The automatic PG screen pointing system comprises a graph signal generator, a pneumoelectric sliding ring, a power source, a computer and a plurality of signal rotating plates corresponding to LCD screen jigs in number. The automatic machine vision adjusting system comprises an industrial camera, a camera installation assembly, a perpendicular movement mechanical arm and a mechanical arm support. A light source support is further arranged on the top of the cabinet base, and a light source is arranged on the light source support and located between the industrial camera and the LCD screen jigs. The signal output end of the industrial camera is connected into the computer, and the control end of the perpendicular movement mechanical arm and the control end of the motor are connected into the computer. The automatic LCD screen detecting cabinet can eliminate the influences of external natural light and dust and impurities in the air on LCD screen defect detection, and meanwhile, the detecting cabinet has high integration degree, and the detecting efficiency of an LCD screen is greatly improved.

The invention relates to piezoelectric intelligent aggregate used for health monitoring of a civil engineering structure, in particular to a structure health monitoring element. The intelligent aggregate comprises a PZT sheet, a waterproof silica gel layer, a shielded wire, a shielded joint and fine aggregate concrete or a mortar cladding layer, wherein, the fine aggregate concrete or the mortar cladding layer is provided outside the intelligent aggregate, the PZT sheet is positioned inside the intelligent aggregate, the waterproof silica gel layer is applied on the PZT sheet, the PZT sheet is connected with one end of the shielded wire, the other end of the shielded wire is connected with the shielded joint, and the shielded joint is connected with a signal emission and collection system. The invention as the main element for health monitoring of the concrete structure has the advantages of high sensitivity, small size, light weight, large strength, and no influence on the overall mechanical performance of the structure.

The invention provides a method and a device for detecting a lane line. The method comprises the steps of carrying out binarization processing on a lane image to be detected so as to acquire a binarized lane image; extracting lane initial edge points in the binarized lane image, and determining the distance between two mutually corresponding lane initial edge points according to coordinates of the lane initial edge points; screening out lane edge point pairs of the binarized lane image from the lane initial edge points according to the determined distance; and determining a lane line in the lane image to be detected according to the screened lane edge point pairs. Through the method and the device provided by the invention, interference points in the binarized image can be filtered, and influences imposed on subsequent lane line detection by the interference points are reduced.

The invention relates to the technical field of surface defect detection, and provides a high-speed rail heavy rail surface defect detection method based on a point cloud method, which comprises the following steps of firstly, constructing a detection platform, and calibrating a color binocular linear array camera; collecting the initial linear array images of the to-be-detected high-speed rail heavy rail under n visual angles and preprocessing the initial linear array images; mapping the two two-dimensional images under each view angle into a three-dimensional depth image, utilizing a two-dimensional image registration method based on phase correlation to provide the initial registration for two to-be-registered point clouds under adjacent view angles, and performing ICP accurate iteration on each pair of point clouds after registration to obtain the complete surface point clouds; and finally, carrying out defect extraction and segmentation on the complete surface point cloud by utilizing a point cloud region growth algorithm taking the normal vector angle and the curvature change quantity as smooth thresholds to obtain the surface defect distribution of the high-speed rail heavyrail to be detected. According to the present invention, the influence of the image acquisition quality and the detection area range on detection can be reduced, the detection efficiency and the detection rate are improved, and the missed detection rate and the false detection rate are reduced.

The invention relates to a multi-parameter field automatic measuring and sorting system. The multi-parameter field automatic measuring and sorting system is provided with a conveyor belt for conveying a cylinder sleeve part to be measured, wherein one side of the conveyor belt is successively provided with an automatic feeding mechanism for automatically transporting each cylinder sleeve part to be measured on the conveyor belt, a first measuring station for carrying out high-precision measurement on a physical dimension and a form and position error of the cylinder sleeve part to be measured, a second measuring station for obtaining the outline information of the cylinder sleeve part as well as an automatic separation mechanism for automatically carrying out classifying selection on the cylinder sleeve parts with different standards according to the measuring results along the length direction of the conveyor belt. The distance between the cylinder sleeve parts on the conveyor belt is the setting distance d, the distance d guarantees that the first measuring station and the second measuring station can carry out the next measurement simultaneously. The multi-parameter field automatic measuring and sorting system provided by the invention has the characteristics of high precision and high efficiency, a workpiece body with the narrow space volume is measured, the high precision measurement is easy to realize, each parameter automatically generate a data report, and the detection data is automatically saved.

The invention provides a TDLAS-based detection device and method of trace CO gas concentration, and solves the problems of deviation of the measured CO gas concentration value and low reliability of measurement results in the prior art. The detection device comprises a function signal generator, a laser device, a collimator, a flowmeter, an air pump, a herriott absorption cell, a temperature sensor, a pressure sensor, a focusing lens, a photoelectric detector, a preamplifier, a digital lock-in amplifier, a microprocessor and a computer, wherein a correcting module is arranged in the microprocessor, and the correcting module is used for correcting the detected gas concentration value. The detection device and method are mainly used for real-time automatic monitoring of trace CO gas, and can effectively avoid the occurrence of death accidents of personnel by poisoning of CO gas.

The invention discloses an image detection method for automatically detecting straws left in grains. The method herein includes the following steps: firstly acquiring an original image; performing adaptive average filtering on the original image, blurring the image, configuring the blurred image as a background image; subtracting the background image with the original image, obtaining an edge enhanced image; converting the edge enhanced image to a gray image, performing adaptive gray equalization, and performing variance binaryzation on the maximum intraclass variance, also acquiring a binaryimage, performing open-operation on the binary image and remove noisy points; performing connected domain labeling on the binary image, performing initial screening on straw targets by comparing the number of the pixel points in the connected domain; drawing the minimal bounding rectangular of the connected domain, screening the straw targets in accordance with the length of the diagonal line of the rectangular; and drawing the profiles of the straws in the original image. According to the invention, the method herein can reduce the influence on the result of the detection by stacking and illumination; and overcomes the difficulty of differentiating similar colors by using the infrared method.

The invention discloses a fluorescence immunochromatographic assay and kit for quantitatively detecting acute myocardial infarction marker, namely, N-terminal pro brain natriuretic peptide (NT-proBNP). The fluorescence immunochromatographic assay for quantitatively detecting the NT-proBNP realizes fluorescence quantitative detection on the basis of optimizing various constituent parts of test paper by using excellent fluorescence characteristic of quantum dots and combining a bicolor marking technology and an immunochromatographic technology. Compared with the conventional colloidal gold immunochromatographic assay, the fluorescence immunochromatographic assay disclosed by the invention has the advantages of good marking stability, low non-specificity, high sensitivity, wide linear range and quantifying accuracy. The fluorescence immunochromatographic kit disclosed by the invention is used for carrying out quantitative detection on the NT-proBNP and detecting whole blood, blood serum and blood plasma samples, can provide reference for diagnosis of cardiovascular and cerebrovascular diseases and can be widely applied to primary hospitals and clinics.

The invention discloses a video target detection method for aerial images of an unmanned aerial vehicle. The method comprises the following steps: carrying out the data preprocessing of each patch (batch) of a training set, and realizing the multi-scale training; adding a feature pyramid and deformable convolution into the feature extraction network ResNeXt to enhance the ability of the feature extraction network; a plurality of detectors are cascaded, the first detector performs target classification and coordinate regression of a suggestion box output by an RPN (Regional Recommendation Network), and the next-level detector further processes the fine suggestion box output by the previous level until the last-level detector ends; and replacing the feature extraction network for retrainingto obtain two models, performing multi-scale test on the two models, and then performing multi-model fusion by adopting a softnms (non-maximum suppression) method to obtain a detection result. The method has high detection precision and strong robustness under the condition that the number of targets in aerial images of the unmanned aerial vehicle is large and the size is small.

The invention discloses an embedded type harmful gas detection system, which comprises a cylinder, a reaction gas chamber; a gas pump, a flow sensor, an illuminating apparatus, an image acquisition instrument, an embedded type ARM9 chip, a lower computer PIC16F877, a touch screen, a porphyrin array sensor, a temperature sensor, a humidity sensor and a controllable heater, wherein the cylinder is used for storing a gas to be detected; the gas pump and the flow sensor are connected in series between the cylinder and the reaction gas chamber; the illuminating apparatus is arranged at the bottom of the reaction gas chamber; the image acquisition instrument is arranged at the top part of the reaction gas chamber; the embedded type ARM9 chip is connected with the lower computer PIC16F877 through an RS232 serial port; the touch screen is connected to the embedded type ARM9 chip; and the porphyrin array sensor, the temperature sensor, the humidity sensor and the controllable heater are arranged in the reaction gas chamber. By using a principle that different color reactions appear when the porphyrin array sensor contacts the gas, and extracting, processing, performing match identification on spectral information, the system finally obtains information on the gas to be detected, displays the information and gives an early warning in time, so the system is suitable for detecting toxic, harmful and volatile organic gasses in the fields of industrial operations, chemical industry, foods, environmental protection, interior decoration, safety and the like.

The invention discloses a video monitoring abnormal behavior detection method and a detections system thereof. The method comprises the following steps: obtaining a video image sequence, and establishing a background model by use of a hybrid Gauss model; according to the established background model, extracting a foreground object area image from the video image sequence by use of a background subtraction method, and dividing the foreground object area image into a plurality of foreground object image blocks; calculating a motion label of each foreground object image block by use of the background subtraction method, and extracting a five-dimensional characteristic parameter of each foreground object image block; determining whether a current foreground object image block is a normal behavior image block by use of an offline SVM classifier, and if the current foreground object image block is a normal behavior image block, ending the process; and if the current foreground object image block is an abnormal behavior image block, determining an abnormal behavior type which the abnormal behavior image block belongs to. Through the method provided by the invention, background modeling is carried out by use of the hybrid Gauss model, a foreground area and a background area can be accurately segmented, and abnormal behaviors in video monitoring images can be accurately detected.

The invention discloses a battery screen printing quality detecting method based on block template matching. Original images of batteries are acquired to build a template data set; images of the batteries are corrected; the corrected images of the batteries are tailored; detecting areas are extracted to reduce information redundancy; battery screen printing images are cut from battery backgrounds;a battery screen printing position is determined; block matching is performed on images to be detected by using block template images in the template data set to judge the matching degree between images of the batteries to be detected and templates; block sub images of the batteries to be detected are compared with built template sub images one by one; difference results are analyzed to obtain battery screen printing defect information; aberration quantitative analysis is performed on each sub image to obtain aberration conditions of each part in global images; and meanwhile, average processing is performed to observe global aberration conditions to finish the battery screen printing quality detection. The method needs no manual intervention, and can realize quick high-precision battery screen printing quality detection.

The invention provides a pipe network leakage loss identification method based on a long-time memory neural network model, comprising the following steps: S1, acquiring DMA inlet data; S2, cleaning the obtained DMA entry data and constructing a multi-scale time data set; 3, establishing a long-time memory neural network model; S4, identifying abnormal flow points based on the constructed multi-scale time data set and the established long-short time memory neural network model; S5, identifying leakage loss of the pipe network according to the identified abnormal flow point. The pipeline networkleakage identification method disclosed by the invention reduces the accident false alarm rate and increases the accuracy of leakage identification.

The invention discloses a wavelet multi-scale crossover operation based sea-sky background infrared small target detection method and belongs to the technical field of image processing. The wavelet multi-scale crossover operation based sea-sky background infrared small target detection method comprises the steps of firstly conducting wavelet decomposition transformation on infrared images under the sea-sky background, then utilizing the low-frequency images obtained through wavelet decomposition to detect a sea antenna through horizontal edge detection and communication direction judgment, and finally conducting mutual energy crossover operation in a sea antenna region to obtain a target to be detected. The wavelet multi-scale crossover operation based sea-sky background infrared small target detection method has the advantages of being accuracy in sea antenna detection, good in adaptability, high in detection speed and good in sea clutter cloud layer interference eliminating capacity and can be widely applied to sea surface target detection, invasion warning, positioning tracking and other aspects.

The invention relates to the field of microbiological detection, in particular to a method and a device for microbiological analysis of a host sample. The method comprises the following steps: (1) performing first filtering processing on a sequencing data set from a host sample by adopting a host genome database so as to remove sequencing data which can be compared with the host genome database from the sequencing data set; (2) performing second filtering processing on the sequencing data set by adopting a homologous database so as to remove the sequencing data which can be compared with the homologous database from the sequencing data set; and (3) comparing the sequencing data set subjected to the first filtering treatment and the second filtering treatment with a microbial genome database so as to determine microbial sequencing data from the microorganisms in the sequencing data set. By applying the method and the device provided by the invention, rapid and accurate analysis and detection of microorganisms in a host can be realized.

The invention provides a road traffic marking detection method and system based on a low-altitude aerial image of an UAV, comprising the steps of acquiring a low-altitude aerial lane image of the UAV;pre-processing the acquired lane image; and calculating the stroke width of the pixel in each connected domain of the pre-processed lane image; screening the connected domains according to the strokewidth of the pixel for sorting the road traffic markings and the roads; and precisely determining the positions of the road traffic markings in the screened connected domains of the road traffic markings by a cubic B spline curve-fitting algorithm based on random sampling consistency. The influences of low-altitude shooting angle, height change, light intensity change, water mark, lane line damage and green belt and shadow on road traffic marking detection are reduced, and the lane lines, arrows, sidewalks, stop lines, etc. are detected. The UAV airborne real-time processing of road traffic marking detection is realized. The invention is applied to the field of traffic information technology and computer vision technology traffic application.

The invention discloses a pulse signal acquisition and data wireless transmission device located on a fingernail. The device comprises a pulse transducer (1), a non-closed clamping elastic piece (2), a hinge (3), a non-closed finger part fixing sleeve (4), a signal conditioning circuit module (5) and a single chip microcomputer acquiring and sending module (6), wherein the pulse transducer (1) is arranged at the front end of the clamping elastic piece (2) which can fix the pulse transducer (1) stably on the fingernail; the clamping elastic piece (2) is connected with the finger part fixing sleeve (4) through the hinge (3) and can rotate along with the movement of a finger part; the pulse signals are processed by the signal conditioning circuit module (5), and processed data are acquired by the single chip microcomputer acquiring and sending module (6) and are transmitted wirelessly to another equipment through Bluetooth of the single chip microcomputer acquiring and sending module (6).

The invention relates to a system for detecting the safety performance of a safety chip under induced error attacks. The system comprises a control unit, a result analysis unit and an attack device, wherein the control unit controls a safety chip encryption and decryption functional unit to carry out encryption and decryption calculation under the induced error attacks; the result analysis unit reads a encryption and decryption calculation result and compares the result with a built-in reference result; the attack device performs the induced error attacks on the safety chip under the control of the control unit. Therefore, the safety chip and the attack device are combined together through the control unit, the calculation result, under the induced error attacks, of the encryption and decryption functional unit is analyzed through the result analysis unit, and after an error is caused on the encryption and decryption calculation result, the attack device is controlled by the control unit to position coordinates of the positions, on the safety chip, of the induced error attacks as fault point coordinates, so that the system effectively detects the safety performance of the safety chip and positions the fault points.