2196 results about "Linear array" patented technology

The invention provides a high-imaging-resolution method for identifying a lead sealing defect part of a cable, and relates to the field of nondestructive testing. Comprising the steps that a linear array probe array is utilized, a plurality of array elements emit ultrasonic waves to a to-be-detected defect part at the same time, and a received echo signal reflects the waveform shape and the waveform phase of the defect. A two-dimensional image is preprocessed through a synthetic aperture focusing technology, a combined image is reprocessed through a phase coherence technology, the quality and accuracy of the image are further improved, and a high-resolution three-dimensional image is constructed in combination with an imaging synthetic aperture focusing technology. The ultrasonic phased array technology, the SAFT technology, the phase coherence technology and the synthetic aperture focusing technology are combined, the imaging resolution is effectively improved, the method has remarkable advantages especially when defect parts in complex structures such as cable lead seals are recognized, the imaging quality is optimized, a two-dimensional image is converted into a high-resolution three-dimensional image, and finally an accurate defect recognition result is obtained.

The invention discloses an intelligent control and detection system for precision coating uniformity, and relates to the technical field of non-contact thickness measurement, the system comprises a spectrum confocal chromatography detection module, a non-contact multi-wavelength full-breadth detection module, an optical flow field dynamic measurement module and the like, a data processing unit is combined with a model to generate an evaluation model, and the evaluation model is used for evaluating the coating uniformity. The system has the advantages that multi-layer coating structure data are obtained in a full-breadth mode through a linear array sensor of the spectrum confocal chromatography detection module, a slurry full-breadth flow velocity vector field is analyzed in combination with the optical flow field dynamic measurement module, and the flow velocity measurement accuracy is improved. And the data processing unit fuses the multi-modal data to generate an evaluation model, and the regulation and control strategy generation module outputs an instruction containing edge air pressure regulation, so that accurate control over coating uniformity is achieved, damage to a base material is avoided, material waste and manual debugging time consumption are reduced, and the production efficiency and the product yield are remarkably improved.

The invention relates to the technical field of grain detection, and discloses a grain broken rice rate online detection method and system based on image analysis. A grain broken rice rate online detection method based on image analysis comprises the steps of intelligent illumination control and high-speed synchronous imaging, self-adaptive image enhancement based on reinforcement learning, multi-stage parallel particle detection and segmentation, multi-modal feature deep fusion, robust classification based on ensemble learning, intelligent quality control and process optimization. And continuously learning and updating knowledge. A clear image is obtained through a multi-angle linear array camera and a stroboscopic light source, a reinforcement learning agent is adopted to carry out adaptive image enhancement, a deep learning network is used to carry out particle detection segmentation, multi-dimensional features are extracted, accurate identification is realized through an integrated classifier, and a digital twin model is established to carry out process parameter optimization. According to the invention, accurate online detection of the broken rice rate of grains can be realized in a high-speed flowing state, and the detection efficiency and accuracy are improved.

The invention discloses a single-bit direction of arrival estimation method and system with signal amplitude recovery, a terminal and a storage medium, and the method comprises the steps: obtaining each snapshot time signal from a signal source of a uniform linear array receiver, introducing a non-zero threshold value to carry out the single-bit quantization of each snapshot time signal, and obtaining a signal model; reconstructing the signal model, replacing a sign function of the reconstructed signal model with a smooth hyperbolic tangent function, and performing reconstruction to obtain a target signal model; based on the target signal model, constructing a single-bit DOA estimation optimization problem by using a truncated Frobenius norm and an L2, 0 norm, and converting the optimization problem into a target optimization problem by using a semi-quadratic technology; and solving a target optimization problem by using a near-end alternating minimization algorithm and a forward-backward splitting algorithm to obtain a signal source estimation direction, a signal source estimation number and a signal estimation amplitude. According to the method, the amplitude of the signal source is accurately estimated while the prior dependence on the number of the signal source is eliminated.

The invention provides a coherent signal arrival direction estimation method and device based on a deep convolutional network, and belongs to the field of array signal processing. The method comprises the following steps: receiving a to-be-detected signal containing a coherent signal by using a uniform linear array antenna to obtain an array receiving data matrix and extract a covariance matrix; forming an input feature vector by right upper triangular elements divided from a diagonal line in the covariance matrix, inputting the input feature vector into a covariance estimation model formed by a deep convolutional network, obtaining an estimation value of the right upper triangular elements under an ideal incoherent condition, and reconstructing the estimation value to obtain a covariance matrix estimation value; and performing characteristic decomposition on the covariance matrix estimation value, and generating a spatial spectrum by using a MUSIC algorithm to obtain an estimation result of the signal arrival direction. According to the method, the noise-containing mixed signal covariance matrix is mapped into the ideal incoherent noise-free signal covariance matrix through a physical constraint supervised learning framework, so that the estimation precision and robustness of the MUSIC algorithm in a coherent scene are improved.

The invention belongs to the technical field of plate shape monitoring and visual analysis, and discloses a stainless steel medium-thickness plate double-face contour measurement method and system, and the method comprises the steps: S1, completing the calibration of a double-line-scan digital camera, and obtaining the upper and lower surface images of a medium-thickness plate; s2, carrying out binocular stereoscopic vision three-dimensional reconstruction to generate a point cloud picture, and converting the point cloud picture into a point cloud picture under the same coordinate system for display; s3, eliminating transverse rotation and deviation according to point cloud center line fitting and plane fitting of the upper surface and the lower surface of the medium-thickness plate; s4, comparing the geometrical relationship between the point clouds of the upper and lower surfaces to quantify local vibration and thickness change, correcting vibration errors, and obtaining the thickness of the medium-thickness plate; and S5, extracting an edge point set from the point cloud image to obtain the overall contour of the medium-thickness plate, and obtaining the position of a shear line based on the width and edge linearity detection of the medium-thickness plate. By the adoption of the technical scheme, the overall contour, overall thickness distribution and accurate shearing position of the medium-thickness plate can be obtained.

The invention belongs to the technical field of underwater target detection, and relates to a multi-target detection method and system based on sparse reconstruction and expectation maximization clustering, and the method comprises the steps: carrying out the preprocessing of echoes received by a linear array in a detection region, and obtaining K independent sample vectors meeting the zero-mean multivariate complex Gaussian distribution; the method comprises the following steps: carrying out uniform and equal-interval sampling on a space sector of a detection area to obtain K theta alternative angles, constructing a K theta-dimensional airspace sparse vector based on the K theta alternative angles, establishing a sparse model for judging whether a target exists in an independent sample vector, and obtaining a joint probability density function; a joint method of expectation maximization clustering based on a sparse model is adopted, and classification and estimation results of parameters in the multi-target scene are obtained through loop iteration; and obtaining an adaptive detector based on likelihood ratio test according to classification and estimation results, and realizing accurate multi-target detection. The method can automatically adapt to different target conditions, and the accuracy and reliability of detection are improved.

According to the floating slab ballast bed sealing strip defect detection method and system based on the multi-modal data, efficient and accurate detection of floating slab ballast bed sealing strip defects is achieved through a multi-modal data fusion technology. The system integrates a linear array camera, an acceleration sensor, a pressure sensor and a controllable vibration source, a multi-dimensional data acquisition system is constructed, and the limitation of a single detection means is effectively overcome. Through collaborative analysis of image data and vibration and pressure data, the accuracy and robustness of defect recognition are remarkably improved. According to the defect evaluation model based on the CNN-LSTM hybrid architecture, implicit features in multi-modal data can be deeply mined, and the defect confidence is accurately quantified. Meanwhile, by combining the preset characteristic parameter threshold value and the classification model, intelligent classification of the deformation defect and the foreign matter defect is realized, and a clear basis is provided for subsequent maintenance.

Disclosed is a method for designing a sparse antenna array that includes determining a first set of separation distances among a plurality of sparse elements along an axis, wherein the first set minimises the highest radiated power of one or more sidelobes, when the beam is pointed at a boresight for a Uniform Linear Array (ULA) that has an equivalent aperture size but greater number of elements with respect to the sparse antenna array, and determining at least one second set of separation distances among the plurality of sparse elements, that minimises the highest radiated power of one or more sidelobes, when the beam is pointed at different angular directions, wherein at least one of these directions is at an edge of the field of view for the equivalent ULA, wherein the first, and at least one second set of separation distances are identical and optimised simultaneously to minimise their combined error function.

The invention discloses a deep-sea self-expanding horizontal array acoustic subsurface buoy, which relates to the field of deep-sea underwater acoustic detection and comprises an expanding platform arranged at the top of the acoustic subsurface buoy, a subsurface buoy main body positioned below the expanding platform and an anchoring structure connected below the subsurface buoy main body, the subsurface buoy body comprises a buoyancy platform arranged below the unfolding platform and an equipment platform arranged below the buoyancy platform. The problem that a hydrophone linear array composed of a plurality of large-scale acoustic linear arrays is folded and automatically unfolded into a cross horizontal array is solved, and integration of a deep-sea acoustic emission system, an acoustic receiving system, an underwater acoustic communication system and an equipment supply system is achieved. The whole subsurface buoy can be quickly and conveniently released and recycled, and high-reliability anchor mooring is achieved.

The invention relates to the technical field of grain sorting and cleaning, in particular to a multi-modal sensor fusion-based grain quality dynamic grading and stone removal cooperative control method, which comprises the following steps of: 1, synchronously collecting by a multi-modal sensor, and generating a synchronous pulse through a high-speed photoelectric trigger when grains fall, a linear array camera is triggered to collect grain surface morphology sequence images, a near-infrared spectrometer collects an absorbance curve and moisture content data, and a piezoelectric weighing platform collects impact pressure waveforms; step 2, dynamic grading decision making; step 3, generation of collaborative parameters; 4, executing and feedback optimization, and issuing the generated parameters to a vibrating screen, a fan and an inclination angle adjusting mechanism to execute control; and detecting the proportion of residual impurities at a stone removal outlet, and triggering the cascade decision-making model to retrain when the proportion exceeds the limit continuously. Through the design, the grain grading and stone removing efficiency and accuracy are remarkably improved, and high-quality product experience and value-added benefits are provided for users.

The invention discloses an acoustically enhanced driven parallel piezoelectric micropump and a driving method thereof. The input layer, the supporting structure and the output layer are sequentially arranged in a stacked mode. The device is characterized by further comprising a plurality of actuators arranged in the supporting structure. The actuators are arranged in a linear array or a matrix. And an input cavity which is connected into a whole is formed between each actuator and the input layer. And a plurality of mutually independent output cavities are formed between each actuator and the output layer. The actuator is used for pumping fluid through self vibration. The input layer is provided with a plurality of inflow structures staggered with the through-flow structures on the actuator. The output layer is provided with a plurality of outflow structures aligned with the through-flow structures on the actuator. The multiple pump flow units are connected in parallel, acoustic vibration is coupled with mechanical vibration, the multiple flow inlet structures are arranged at the multiple antinode points of acoustic resonance, flowing of fluid is further enhanced, and the air pumping efficiency is improved.

The invention provides a wafer surface defect detection method and device based on machine vision and a storage medium. Firstly, a linear array industrial camera is used for carrying out image collection on the surface of a wafer, and original image data of the wafer are obtained after synthesis; carrying out image preprocessing, and carrying out image enhancement and feature extraction on the image; because the gray values of the defects and the background are different, threshold segmentation is carried out by using a binarization method to obtain a defect set of the wafer. Classifying and detecting defects, wherein the defects include but are not limited to scratches and particles; preferentially extracting defect subsets with obvious defect features, and removing the defect subsets from the overall defect set; then, searching scratch defects, wherein particle defects are obtained after all other defects are removed; the pixel coordinate positions of various defects are taken as the reference, and the defects are represented on the Map by using different colors. The algorithm designed by the invention is high in recognition precision, high in speed and high in adaptability, and manpower is greatly saved.

The invention discloses a server liquid cooling cabinet group heat dissipation system with phase change energy storage, which comprises two linear array type server liquid cooling cabinet groups, two cooling capacity distribution units, two heat pipe air conditioner all-in-one machines, a multi-channel cold liquid distributor, a multi-channel hot liquid backflow device and the like, the all-in-one machine comprises a heat pipe natural cold source refrigerating loop and a compressor mechanical refrigerating loop, the two refrigerating loops independently or jointly provide an outdoor cold source, heat of all the liquid cooling cabinets is transmitted to the all-in-one machine in the cold capacity distribution unit, and therefore the heat is transmitted out of the machine room. A phase change energy storage flow channel is preferably arranged on the primary side of the intermediate heat exchanger in parallel, cold storage and buffering are achieved by switching a channel, a cold storage and cold release intelligent control strategy based on a lag temperature difference threshold value is constructed, a main cold source linkage and cold source switching smooth transition mechanism is achieved, the thermal stability, operation continuity and response capacity of the system are improved, and the system reliability is improved. The liquid cooling heat dissipation device is suitable for data center liquid cooling heat dissipation scenes with high heat density, high energy efficiency and high reliability requirements.

The invention discloses a two-dimensional and three-dimensional fused fabric defect detection method, and belongs to the technical field of machine vision recognition. The technical problem of limitation of a traditional two-dimensional machine vision system is solved. According to the technical scheme, the method is characterized in that multi-angle light source imaging is carried out, a detected fabric is polished through light sources at different angles, and two-dimensional images of illumination at different angles are obtained through shooting of a linear array camera; and two-dimensional and three-dimensional information fusion: carrying out pixel-level fusion on the shot two-dimensional grey-scale map and the reconstructed gradient map and depth map with the three-dimensional information, carrying out high and low frequency decomposition and fusion on the two-dimensional information and the three-dimensional information through non-subsampled contourlet transformation, and fusing the two-dimensional defect feature information and the three-dimensional defect feature information into one map. After the non-subsampled contourlet transform decomposes the image, the frequency domain space is divided into wedge-shaped directional sub-bands; defects are identified based on the deep learning model. The method has the effect of improving the accuracy of textile quality detection.

The invention relates to the technical field of integrated circuit board finished product detection, in particular to a computing processing module finished product monitoring method and system, and the method comprises the steps: scanning a plurality of computing processing module finished products passing through a finished product conveying belt in real time through a linear array laser scanner disposed above the finished product conveying belt, and carrying out the real-time monitoring of the plurality of computing processing module finished products; further obtaining a spectrum pattern corresponding to each calculation processing module finished product; identifying local spectrum patterns corresponding to the plurality of chip assemblies in the spectrum pattern; and sequentially detecting whether each local spectrum pattern has a plane flaw, if so, evaluating the damaged surface of the plane flaw, and evaluating the damage degree of the damaged chip assembly based on the proportion of the damaged surface. According to the monitoring method, the spectral pattern representing the height information is generated in real time through the linear array laser scanner, the local areas of the multiple chip assemblies are accurately recognized in combination with the RGB color threshold value and the image feature extraction algorithm, interference caused by dense arrangement of the multiple assemblies is effectively avoided, and the positioning precision and the anti-noise capacity of plane defect detection are improved.

The present invention discloses a wide microstrip antenna array based antenna system for millimeter wave applications. The microstrip antenna array based antenna system of the present invention includes plurality of patch antennas and each of the patch antennas includes a U-slot of unequal arms. This patch antenna with the U-slot acts as the array element. The array elements are placed in a linear array at a distance of λ / 2 from each other on the antenna substrate to achieve desired scan performance. The microstrip corporate feed network with T-junction power divider is used. The antenna elements are fed by electromagnetic coupling with the feed network. The antenna array with dummy elements on both sides of the linear array together with an extra ground plane is used to achieve improved array performance with high main lobe gain and reduced side lobe.

The invention provides a device for detecting corrosion resistance of a nano-deposited coating of a color steel coil, and relates to the field of corrosion resistance detection. A driving assembly is fixedly arranged at the top of the supporting frame; the driving assembly comprises a closed box; the closed box is fixedly arranged on the top of the supporting frame. An auxiliary mechanism is movably arranged in the closed box, and a detection structure is fixedly arranged at the bottom of the auxiliary mechanism; an adjusting assembly is arranged on the outer side of the detection structure, an acquisition mechanism is fixedly arranged on the outer side of the adjusting assembly, and through the acquisition mechanism and the adjusting assembly, residual liquid on the surface of the nano-deposited coating can be quickly removed when image acquisition is performed on the nano-deposited coating, so that interference of the residual liquid on image acquisition is effectively avoided; the definition, integrity and accuracy of the collected image are guaranteed, and the problem that residual liquid on the surface of the nanometer deposition coating seriously interferes with the image collection process of the linear array camera is solved.

The invention relates to the technical field of satellite internet, and discloses a low-orbit satellite phased array multi-beam interference modeling and suppression method and system, and the method comprises the steps: selecting a Kaiser window as a core filtering method, and achieving the optimization of beam characteristics through the dynamic adjustment of a shape parameter beta; generating an initial beam directional diagram based on a digital phase matching method, and multiplying the Kaiser window function coefficient by the excitation weight of the 64-array-element linear array element by element to realize spatial domain weighted filtering; the method comprises the following steps: constructing a training data set containing multi-scene interference characteristics, calculating a corresponding covariance matrix and an accurate inverse matrix thereof to form a sample pair, designing a deep neural network architecture, inputting a flattened covariance matrix vector, and learning a complex nonlinear mapping relation from the covariance matrix to the inverse matrix through a multi-layer full-connection structure; a mean square error is used as a loss function to constrain network output precision, and a multi-beam interference system model is constructed; according to the invention, stable and efficient communication of the low-orbit satellite system in a complex electromagnetic environment and under rapid channel change is ensured.

The invention discloses a linear array data processing method for surface pulse pressure wave number-frequency spectrum measurement. A one-dimensional linear array is adopted to carry out a pulsating pressure measurement test, a model surface time domain signal is obtained, an array signal cross-spectrum matrix is calculated, and a flow direction wave number-frequency spectrum result is obtained through calculation by taking the cross-spectrum matrix as input. The method is directly obtained through a turbulent boundary layer pulsating pressure flow direction wave number-frequency spectrum physical definition formula and has direct relevance with the physical definition formula, the cross spectrum matrix method is applied, the calculation and analysis capacity is greatly improved, parallel calculation can be adopted in cross spectrum matrix calculation, a direct discrete algorithm and an average discrete algorithm, and the calculation speed is greatly improved.

The invention relates to the field of artificial intelligence and computer vision, discloses an AI vision-driven polyester fabric pilling detection method and system, and aims at solving the problems that traditional manual visual inspection is low in efficiency and high in subjectivity, and existing machine vision is insufficient in sensitivity to low-contrast tiny defects. The method comprises the steps that fabric images are collected through a high-resolution linear array camera and a multi-channel self-adaptive LED light source, after non-uniform correction and self-adaptive filtering, the fabric images are input into a three-level convolutional network to extract multi-scale texture features, and then unified representation is generated through cross-scale attention fusion; according to the technical scheme, high-precision recognition of pilling with the diameter smaller than 0.5 mm is achieved, the detection rate is increased by 42%, the false alarm rate is lower than 3%, full inspection of a 60-meter / minute production line is supported, structured quality data are output, and the intelligent and quantitative level of online quality inspection in the textile industry is remarkably improved.

The invention relates to the technical field of size detection, in particular to a component size detection system based on machine vision, which comprises an interference area identification module, an edge repair module, a structure clustering module, a distortion analysis module and an attitude restoration module. According to the method, the gray level change trend of each pixel and the adjacent points in the image is obtained, the high-reflection suspected interference area is identified, the edge response cancellation processing is executed, and linear interpolation compensation is carried out by combining the effective edge response values of the adjacent areas; after a continuous contour trend is constructed, clustering and sub-grouping division are carried out according to an included angle difference trend between contour geometric vectors, a three-axis direction response linear array variation amplitude is extracted to construct a distortion curvature mapping table, existence of non-equal-interval deformation is judged, reverse angle compensation is applied, and a three-axis intersection point convergence process is completed through a nonlinear optimization algorithm; and according to the three-dimensional coordinates, calculating a geometric dimension and outputting a detection result, thereby realizing self-adaptive detection of different postures and surface interference component dimensions.

The invention discloses a multi-scale linear array camera splicing method and system based on point cloud. The method comprises the following steps: completing acquisition and preprocessing of point cloud data and image data of a target area; determining an overlapping region range between adjacent images; extracting spatial structure characteristics in the point cloud data, and performing multi-scale hierarchical decomposition on the point cloud through a multi-scale segmentation method; meanwhile, multi-scale image feature extraction is carried out on the images of the linear array camera; solving gradients in X and Y directions by adopting an optical flow method aiming at any pixel in the overlapping region, and calculating a motion vector between the pixels; fusing the optical flow information obtained under each scale, and constructing a globally consistent optical flow vector field; according to the fused optical flow vector, calculating to obtain a geometric transformation matrix of the whole overlapping region; and after image transformation and alignment are completed through the transformation matrix, fusion processing is carried out on overlapped areas. And the unification of the visual effect and the spatial integrity of the spliced image is ensured.

The invention discloses a multi-section type linear array X-ray source static real-time CT imaging system and an imaging method, and the multi-section type linear array X-ray source static real-time CT imaging system is provided with a scanning device and a ray source control unit; the scanning device is provided with a multi-section X-ray source array and a detector, the transmitting end of the multi-section X-ray source array faces the detector, and the multi-section X-ray source array is connected with the ray source control unit. The multi-section X-ray source array has the advantages that the multi-section X-ray source array can freely adjust the length and the included angle of the whole section, and the multi-section X-ray source array is suitable for a large-size measured object; 2, the multi-section X-ray source array can use fewer X-ray sources to realize the same wide view field, the design is more free, and the expansion is easier; 3, the distribution of ray sources can be relatively dispersed, the crossing of ray paths is less, and the interference of scattered signals can be obviously reduced; and 4, the exposure strategy of the scanning device can be controlled through the radiation source control unit, and the radiation dose utilization rate is optimized.

The invention relates to a linear array push-broom imaging spectrum system based on an optical fiber device, which comprises a spectrograph, an objective lens and an optical switch, and is characterized in that the view field of a telescope objective is fully utilized, and a second optical fiber of a linear array is coupled to the focal plane of the objective lens to fill the one-dimensional view field of the objective lens. The second optical fiber is connected with the optical switch, the on-off of the sub optical fibers corresponding to different view fields is rapidly controlled through the optical switch, and the flight motion of the aerial carrier platform is matched, so that rapid time-sharing detection based on different view fields of the same high-speed spectrometer can be realized, and an imaging spectrum detection effect similar to push-broom imaging detection can be realized. Compared with a conventional linear array sweep imaging spectrum system, the optical fiber array and the optical switch device are adopted to replace a sweep mechanism, sweep mechanical moving parts are not needed, the size and the weight of the imaging spectrum system are greatly reduced, and the influence on the flight stability of an aerial carrier platform is extremely small.

The invention discloses a multi-light-spot anti-interference deformation measuring device and method based on stroboscopic energy spectrum.The multi-light-spot anti-interference deformation measuring device comprises a point light source, a first cylindrical mirror is arranged below the point light source, a first linear array sensor is arranged below the first cylindrical mirror, and the first linear array sensor is electrically connected with a data acquisition device; light emitted by the point light source passes through the first cylindrical mirror and then is focused into a line segment to be imaged on the first linear array sensor, and the first linear array sensor converts optical signals of the focused line segment into digital signals and outputs the digital signals to the data acquisition device. According to the multi-light-spot anti-interference deformation measuring device, a multi-dimensional displacement sensor measuring system is established by applying the cylindrical mirror, the linear array sensor and the point light source, a displacement measuring system with high precision, high resolution and high sampling rate can be realized, and the multi-light-spot anti-interference deformation measuring device is few in related components, small in size, simple to maintain, low in cost, high in system stability and easy to popularize. The method is more suitable for outdoor multi-point monitoring of engineering structures.

The invention discloses a method and a system for predicting the propagation direction of a seismic wave field excited by a high-speed rail seismic source. The method comprises the following steps: simplifying the high-speed rail seismic source into vertical downward impact force of wheels on the ground; taking the Ricker wavelet as a deformation function of force, summing all wheels of the high-speed rail train to obtain a high-speed rail sub-seismic source function, and performing Fourier transform to obtain an amplitude spectrum; the method comprises the following steps: modeling uniformly arranged sleepers or piers of a high-speed rail line into a uniform linear array, exciting seismic waves at each array element in the uniform linear array, and summing seismic wave signals of each array element at a far-field receiving point to obtain an array factor; a radiation factor formula of the P wave and the S wave of the seismic wave field of the high-speed rail seismic source is obtained by combining the radiation pattern factors of the seismic P wave and the S wave excited by the single-point concentrated force; drawing a theoretical interference directional diagram of seismic waves; and establishing a model for simulation, comparing a theoretically predicted interference pattern with a simulation data wave field snapshot, and keeping the predicted direction consistent with the simulated wave field direction, thereby verifying the rationality and correctness of the proposed method.

Base station antennas include a reflector, a first linear array of radiating elements extending forwardly from the reflector, the radiating elements in the first linear array configured to operate in a first frequency band, and a second linear array of radiating elements extending forwardly from the reflector, the radiating elements in the second linear array configured to operate in the first frequency band. These antennas further include an isolation wall that is positioned in between the first linear array of radiating elements and the second linear array of radiating elements. This isolation wall comprises at least one partially reflective surface that is configured to reflect, on average as a function of frequency, between 20% and 80% of incident radiation in the first frequency band.

The invention discloses a non-circular interference suppression method for co-prime array satellite communication in a non-uniform noise environment. The non-circular interference suppression method comprises the following steps: 1, removing diagonal elements of a non-circular signal covariance matrix of co-prime array satellite communication; 2, constructing a differential common array and interpolating the differential common array into a differential virtual uniform linear array; 3, restoring a non-circular signal vector of the interpolation difference virtual uniform linear array; 4, a summation common array is constructed and interpolated into a summation virtual uniform linear array; 5, restoring interpolation and summing the non-circular signal vector of the virtual uniform linear array; 6, estimating a satellite communication non-circular signal direction; 7, estimating a signal plus interference covariance matrix, a non-uniform noise covariance matrix and a non-circular coefficient of a non-circular signal; 8, estimating satellite communication non-circular signal power; 9, reconstructing a co-prime array interference and noise covariance matrix; and 10, calculating a satellite communication co-prime array amplification weighting vector. And the non-circular interference suppression performance of satellite communication in a non-uniform noise environment is improved.

The invention relates to an intelligent wind power blade defect identification method based on phased array detection, which aims at the anisotropic characteristic of a wind power blade glass fiber reinforced composite material, and can flexibly adjust the sound beam angle, effectively penetrate through the material and inhibit the scattering of an ultrasonic signal through a low-frequency linear array probe with characteristic parameters and an electronic focusing technology. Defects in different directions such as impurities perpendicular to layers, cracks in the fiber direction, wrinkles and the like can be accurately detected, meanwhile, by combining wavelet packet transformation noise reduction and a feature extraction algorithm, the distinguishing capacity of defect echoes and noise in complex materials is remarkably improved, the method is adaptive to complex structures and material characteristics of wind power blades, the application range of the detection technology is expanded, and the detection efficiency is improved. An ultrasonic phased array detection technology and an intelligent algorithm (a K-means clustering algorithm and a YOLOv8 target detection model) are deeply fused, the limitation that traditional detection depends on manual analysis is broken through, and a full-process automatic detection system of data acquisition, feature extraction and intelligent recognition is constructed.