48results about How to "Simplify the algorithm process" patented technology

The invention belongs to the field of laser scanning surveying and mapping, and provides a geometric correction method for a vehicle-mounted scanning point cloud in a tunnel GNSS denial environment, which can solve the problem of geometric correction of the point cloud of non-rigid body deformation in a tunnel area, thereby meeting the requirement of high-precision point cloud acquisition. The method comprises the following steps: firstly, arranging targets in groups along a tunnel extension direction; secondly, obtaining point cloud data through surveying and mapping of a vehicle-mounted scanning system, and obtaining calibration point coordinates of each target through actual measurement; thirdly, extracting three-dimensional coordinates of homonymy points of the corresponding targets from the point cloud data obtained by the vehicle-mounted scanning system; then, calculating conversion parameters between actual measurement target coordinates and point cloud target coordinates in groups, correcting track positions of the vehicle-mounted scanning system corresponding to each group of targets, and complementing tracks of the mobile measurement system in other track areas to be corrected through adoption of an interpolation method; and finally, re-resolving by using the corrected accurate track of the mobile measurement system to obtain high-precision long tunnel point cloud data.

The present invention provides an image de-noising method and device. The method comprises: establishing a linear shift variant filtering linear function model qi=akIi+bk, and A-inversed I belongs to wi, wherein qi is the brightness value of a pixel point in the image after denoising, and Ii is a brightness value of the pixel point in an original image; performing filtering process of the original image, and obtaining an image to be filtered; employing the principle of infinite approaching of the brightness value qi of the pixel point in the image after denoising and the pixel value pi of the pixel point in the image to be filtered, and calculating the expression of the coefficient ak and the coefficient bk; performing window mean value filtering processing and pixel point operation of the original image and the image to be filtered according to the expression of the coefficient ak and the coefficient bk, and obtaining the concrete values of the coefficient ak and the coefficient bk in a window wk; and substituting the concrete values of the coefficient ak and the coefficient bk in the window wk into the function model qi=akIi+bk, and obtaining a de-noising image. The image de-noising method and device use window mean value filtering and pixel point operation to collect the calculation of the window target value, the algorithm flow is simple and easy to realize, and the linear function model can maintain the image edge characteristics.

The invention relates to a non-invasive load monitoring method based on equipment transfer learning, and the method comprises the steps: collecting the load amplitude data of household electrical appliances and the household total load amplitude data, and building an original data set; normalizing the data of the original data set; constructing an initialization sequence-to-point CNN model to construct a transfer learning equipment selection model, and selecting initial data and target data of transfer learning; performing a training process and a verification process on the initialization sequence-to-point CNN model by adopting the initial data to form a transfer learning initial model; finely adjusting a full connection layer of the initial model by adopting the target data to form a transfer learning target model; and inputting the normalized household total load amplitude data into the transfer learning target model to obtain the power amplitude of the target equipment, thereby realizing non-intrusive load monitoring of the target electrical equipment. According to the invention, the model training time is reduced, and the economic cost and time cost of non-intrusive load monitoring are reduced.

The invention discloses a millimeter wave sparse array remote monitoring imaging method and system, and belongs to the technical field of millimeter wave three-dimensional holographic imaging. The method comprises the following steps: S1, obtaining an echo signal; S2, performing interpolation operation; S3, performing fast Fourier transform; S4, selecting a distance plane; S5, performing frequencydomain matched filtering; S6, performing fast Fourier inverse transform; S7, performing time domain matched filtering; S8, performing coherent accumulation; and S9, obtaining a three-dimensional complex image. According to the millimeter wave sparse array remote monitoring imaging method and system disclosed by the invention, a millimeter wave sparse array full-electronic scanning imaging systemis adopted, and compared with a traditional optical machine scanning far-field imaging system, the millimeter wave sparse array full-electronic scanning imaging system has the characteristics of smallelectromagnetic wave flicker effect, high image signal-to-noise ratio and large imaging field range; meanwhile, compared with a traditional time domain type imaging method, the adopted signal processing imaging method has the advantages of less hardware resources and less storage space, the algorithm process is simple and easy to understand, the algorithm main body only comprises the main body process of fast Fourier transform, matched filtering and coherent accumulation, and the calculation efficiency is higher than that of the traditional time domain type algorithm.

The invention provides a robot teaching recording system which is efficient, high in applicability, speedy and low in cost, and has few parts, provides a teaching method based on the system, and belongs to the field of robot teaching. The system comprises optical beacons, an IMU sensor, a camera, a data fusion calculation unit and a robot control cabinet, wherein the robot control cabinet is a control driving unit for a robot and receives data or files output by the IMU sensor and a visual fusion calculation unit; the camera is used for acquiring the positions of the beacons; IMU data are fused by the data fusion calculation unit; and the positions, speeds and accelerated speeds of the optical beacons and process actions at corresponding positions are output in a fused manner finally by the data fusion calculation unit. The provided teaching system and method can be applied to following occasions: teaching actions and process can be finished by teaching workers, robot sensors do not require to be added or a mechanical structure does not need to be changed, and a robot system only needs to receive external commands or external files.

The invention belongs to the technical field of the inverter, and particularly relates to an SHEPWM control system and method for a multi-level inverter. The SHEPWM technology of the multi-level inverter is realized by adopting a particle swarm-bacteria foraging optimization algorithm (PSO-BFO), so that the output waveform is ensured while the switching angle is worked out accurately, and the purpose of eliminating special harmonics, simplifying the operational process and shortening the calculation time is achieved. The SHEPWM control system for the multi-level inverter comprises a DPS processing circuit, a voltage sampling circuit, an isolation driving circuit, a switching power supply circuit and a voltage sensor, wherein the voltage sensor, the voltage sampling circuit, the DPS processing circuit and the isolation driving circuit are connected in sequence; and the output port of the isolation driving circuit is connected with the control port of the switching device of a T-type three-level photovoltaic grid-connected inverter.

The invention relates to a single robotic fish behavioral control strategy based on a three-dimensional fuzzy PTP algorithm and belongs to the field of intelligent control technology. The strategy relates to robotic fish ball heading movements, provides a PTP algorithm which faces to division and improvement for ball heading points in different regions, designs behavioral strategies corresponding to different regions and provides a basic behavioral sequence in robotic fish ball heading process. A control algorithm which faces to different regions is adopted to realize a simple task which is to let a robotic fish to head a ball to reach a target point; pool regions are divided specifically according to ball heading points, and ball heading movement behaviors of a robotic fish in corresponding regions are designed. According to the invention, the strategy is used for the robotic fish in complex underwater environments with barriers, noise and the like; three-dimensional fuzzy processing is performed on speed, angle and sampling time, the mode turns to control mode, it is convenient to directly use the robotic fish control mode, algorithm flow is greatly simplified, algorithm complexity is reduced, and the strategy also has advantages of fast data update speed, high precision and good real-time capability.

The invention discloses a macaque emotion recognition method and system based on time-frequency double-branch features, and the method comprises the steps: inputting collected macaque sounds into a pre-established and trained macaque emotion recognition model, and obtaining corresponding accurate emotion types; wherein the accurate emotion types comprise friendly emotion, attack or threat emotion, falling or compliance emotion and non-emotion; and the macaque emotion recognition model is used for extracting intermediate features of macaque sounds, performing separation and extraction on time domain features and frequency domain features based on time-frequency double branches, and determining corresponding accurate emotion categories through fusion and classification. According to the method and the system, original sound data is used as network input, emotion-related intermediate features are extracted from sound, voice preprocessing is not needed, the algorithm process is simplified, then time domain and frequency domain features are separated and extracted, compression features of the sound are obtained from different angles, the time domain and frequency domain features are fused, the expressive power of the features is enhanced, and the identification accuracy is effectively improved.

The invention discloses a vehicle detection method and device, a storage medium and electronic equipment, and the method comprises the steps: inputting a to-be-detected target image into a feature extraction layer of a vehicle detection model during vehicle detection, and extracting a first feature map of the target image; then, detecting a vehicle detection frame, a license plate detection frame and a vehicle type in the first feature map through a target detection layer, and performing up-sampling on the first feature map to obtain a second feature map; and then, determining a license plate region feature map from the second feature map based on a license plate detection frame, and recognizing the license plate number of the license plate region feature map through a number recognition layer. And finally, according to the vehicle detection frame, the license plate detection frame, the identified vehicle type and the license plate number, determining a vehicle detection result of the target image. The vehicle position, the vehicle type, the license plate position and the license plate number in the target image are detected through the vehicle detection model, manpower resources are saved, and the detection efficiency is improved.

The invention discloses a high-voltage line detection method combining deep semantic segmentation with Hough transform, and the method employs a deep learning semantic segmentation technology to makethreshold judgment of a target through combining with the Hough transform, achieves high-performance detection of a high-voltage line, can achieve further confirmation of a detection target through the threshold after the Hough transform, and improves detection accuracy. The false target of an original image is suppressed, and the dual effects of target detection and false alarm reduction are achieved. According to the technical scheme disclosed by the invention, an algorithm flow is concise, detection rate is higher than that of a traditional parameter adjustment method, the false alarm rateis further reduced through Hough transform, and the problem of excessive false alarms of the high-voltage line caused by semantic segmentation is avoided.

The invention provides a robot teaching recording system which is efficient, high in applicability, speedy and low in cost, and has few parts, provides a teaching method based on the system, and belongs to the field of robot teaching. The system comprises optical beacons, an IMU sensor, a camera, a data fusion calculation unit and a robot control cabinet, wherein the robot control cabinet is a control driving unit for a robot and receives data or files output by the IMU sensor and a visual fusion calculation unit; the camera is used for acquiring the positions of the beacons; IMU data are fused by the data fusion calculation unit; and the positions, speeds and accelerated speeds of the optical beacons and process actions at corresponding positions are output in a fused manner finally by the data fusion calculation unit. The provided teaching system and method can be applied to following occasions: teaching actions and process can be finished by teaching workers, robot sensors do not require to be added or a mechanical structure does not need to be changed, and a robot system only needs to receive external commands or external files.

The present invention provides a white balance method and device, the method includes the following steps: firstly receive the RGB data stream, and calculate the red gain and blue gain of the pixels in the data stream; then calculate the average red gain and the blue gain of all skin color points The average blue gain; then map the average red gain and average blue gain to the skin color characteristic color temperature curve to determine the color temperature value of the current environment; then map the current environment color temperature value to the ideal white point characteristic color temperature curve to determine the ideal red color The gain value and the ideal blue gain value; then according to the determination of the ideal red gain value and the ideal blue gain value, the white balance correction is performed. The present invention adopts the white balance method of mapping the skin color feature to the characteristic color temperature curve of the ideal white point, which can completely eliminate the interference of the skin color to the white balance, and effectively solve the problem of color cast in large-area skin color photographing pictures.

The invention provides a method for determining a whole-cycle ambiguity of a three-frequency carrier phase of a BeiDou navigation system. The method comprises the steps of by utilizing the advantage of high determination accuracy rate of a whole-cycle ambiguity of a BeiDou ultra-wide lane carrier phase combination, firstly determining whole-cycle ambiguities of two groups of ultra-wide lane carrier phase combinations, and correcting a pseudo-range initial value through the whole-cycle ambiguities of the ultra-wide lane carrier phase combinations; meanwhile, removing influences from an orbit error, a clock error and an ionized layer error in original observed quantity by combining with carrier phase observation quantity of geometry-free de-ionized layer composed of a narrow lane carrier phase combination, so as to enable the resolution of the whole-cycle ambiguity of the narrow lane carrier phase combination to be only affected by random measurement noise; taking an average value of combined observation quantity of multiple epochs to remove random noise error and determine the whole-cycle ambiguity of the narrow lane carrier phase combination; finally, determining the whole-cycle ambiguity of the carrier phases of three frequencies B1, B2 and B3 by resolving a system of linear equations which is composed of the whole-cycle ambiguities of the two ultra-wide lane carrier phase combinations and the whole-cycle ambiguity of the narrow lane carrier phase combination.