245results about How to "Overcome accuracy" patented technology

The invention discloses a positioning device and a positioning method for fault detection of local discharge, and the positioning device comprises two sensors, a local discharge detection positionfinder and a computer, wherein, the two sensors consist of an electromagnetic sensor and a resonant ultrasonic sensor; the local discharge detection positionfinder is internally provided with double-signal paths in parallel; the positioning device also comprises a data acquisition card and a liquid crystal display (LCD) which are connected with the output ends of the double-signal paths; one path of the double-signal paths is an electromagnetic path connected with a peripheral electromagnetic sensor, while the other path thereof is an ultrasonic channel connected with a peripheral resonant ultrasonic sensor; one path is displayed by the LCD connected with the acquisition card, and the other path is sent to a peripheral computer through data wires after signal received by the electromagnetic path and the ultrasonic path is processed by the acquisition card. The test method is simple and flexible, has easy operation and strong anti-interference performance, overcomes the defect of single detection method for local discharge in the prior art, and can realize accurate detection and position on local discharge fault points in power equipment.

The invention discloses a human body information monitoring and processing system and a human body information monitoring and processing method. The system comprises a human body information monitor, a cloud computing data interaction center, a motion wristwatch and a user terminal, wherein the human body information monitor is used for monitoring physiological and unphysiological signals of moving human bodies in real time in a multi-mode and multi-channel mode and transmitting signal data to the cloud computing data interaction center; the cloud computing data interaction center is used for analyzing the received signal data and transmitting the analyzed result data to the motion wristwatch and the user terminal respectively; the motion wristwatch is used for making a motion plan according to the received result data and performing warning prompt or adjusting the made motion plan in real time; and the user terminal is used for displaying the received result data so as to realize human-machine interaction. By the system and the method, the comfort degree of motion signal monitoring equipment and the accuracy of monitoring are improved, the motion plan is made for the human bodies through the monitored physiological and unphysiological signals and network on-line expert consultation is provided for the human bodies to trace body health conditions.

The invention discloses a method for partitioning communities in a complex dynamic network by virtue of multi-objective local search based on decomposition, and the method is mainly used for solving the problem of poor community partitioning accuracy in the course of processing the complex dynamic network in the prior art. The method is implemented through the following steps: (1) determining objective functions; (2) constructing an initial solution population, and initializing individuals in the solution population by a neighborhood real-number encoding method; (3) sequentially selecting the individuals from the solution population and then carrying out cross variation on the individuals to obtain progeny individuals; (4) updating the solution population by virtue of the progeny individuals; (5) locally searching and updating the solution population; (6) judging whether the population evolution process is terminated: if iterations reach the preset times, executing a step (7), otherwise, transferring to the step (3); and (7) selecting the optimum community partition according to the maximum module density principle. The method disclosed by the invention has the beneficial effects that two objective functions can be optimized at the same time, synchronous analysis of community partition and community evolution is realized, the community partitioning accuracy is improved, and the problem of detection of a community structure in the complex dynamic network can be solved.

The invention provides a digital isothermal nucleic acid detecting device which comprises a micro-fluidic chip, a temperature control system and a pressure driving system. A microfluidic channel for microsphere forming and a reaction section for nucleic acid amplification are formed by the micro-fluidic chip through sequential laminating of a substrate layer, a channel layer arranged on the substrate layer and a cover plate layer arranged on the channel layer. The temperature control system comprises a lower press plate for applying pressure to the substrate from bottom to top and an upper press plate for applying pressure to the cover plate layer from top to bottom, and temperature sensing chips and temperature control heating elements for heating the reaction section are arranged in the upper press plate and the lower press plate. The pressure driving system is connected to the micro-fluidic chip and used for applying pressure to the channel layer in the micro-fluidic chip so that liquid to be detected can flow into the reaction section from the inflow end. According to the scheme, microsphere preparation and nucleic acid amplification and detection are integrated through the digital isothermal nucleic acid detecting device and the detection method, and the advantages of being high in sensitivity and the like are achieved.

The invention provides a municipal drainage pipe network decision evaluation method, which comprises the following steps of building a drainage pipe network model and conducting the model analysis on the basis of online monitoring; auditing a pipe network maintenance plan on the basis of the model analysis; diagnosing the current situation of a pipe network on the basis of the model analysis; and making strategic decision for the pipe network plan on the basis of the model analysis. By utilizing the method, a comprehensive municipal drainage pipe network decision evaluation method based on a global information system (GIS) technology, a monitoring technology and a model technology is provided, and effective integration of multiple technologies is realized, so that the decision evaluation process not only has a professional calculation and analysis function provided by the drainage pipe network model, but also has a direct display and space analysis capacity by utilizing the GIS technology and utilizes the monitored data to improve the model accuracy and confidence, so that the defects that the traditional decision analysis result is difficult to read, the evaluation mean is simple, the accuracy of the model is difficult to guarantee and the like can be overcome, and the decision evaluation level of the drainage pipe network can be greatly improved.

The invention discloses a multi-index fusion-based driver fatigue detection method. The method comprises the steps of firstly, acquiring a face image of a driver in a normal state to carry out human face positioning, selecting a first face feature point, and determining a driver eye fatigue judgment threshold value through the first face feature point; acquiring a face image of the driver in a driving state, carrying out human face positioning, selecting a second face feature point, and calculating an eye fatigue index, a mouth fatigue index and a head posture fatigue index of the driver; andin combination with the eye fatigue index, the mouth fatigue index and the head posture fatigue index, judging whether the driver is in fatigue driving or not. According to the method, various physiological characteristics of a person in a fatigue state are comprehensively considered, and the fatigue state of the driver is detected in a multi-index fusion mode, so that the fatigue state of the driver can be detected more accurately, and the problems of easy disturbance and low identification accuracy of a traditional single-index detection method are effectively solved.

The present invention relates to a needleless pain-alleviating apparatus. It is characterized by that said needleless pain-alleviating apparalus can output dilatational wave with 2H2 low-frequency and 100 plus or minus 10H2 high-frequency and uses said dilalational wave as electric pulse stimulation signal so as to attain the goal of alleviating pain. Said needleless pain-alleviating apparatus includes the following several portions: microcontroller, low-frequency signal generator, high-frequency signal generator, low-pass filter and mos switch. Said invention also provides the working principle of said meedleless pain-alleviating apparatus and its concrete operation method.

The invention relates to a micro-CT technology-based reservoir core three-dimensional entity model reconstruction method. The method includes the following steps that: a micro-CT technology is utilized to scan a reservoir core, so that a sectional scanning image of the reservoir core can be obtained, so that a CT image of a real reservoir core can be obtained, and a watershed algorithm is adopted to perform image segmentation on the CT image, so that the three-dimensional data of the image can be obtained; a Marching Cubes algorithm is adopted to generate a three-dimensional surface model of the reservoir core; and with the three-dimensional surface model of the reservoir core adopted as constraints, a constrained Delaunay tetrahedralization algorithm is utilized to generate the three-dimensional entity model of the reservoir core. According to the method of the invention, the three-dimensional entity model of the real reservoir core is established based on the sectional scanning image of the reservoir core, so that the obtained three-dimensional entity model of the reservoir core is more approximate to the structure of a real reservoir core. The method of the invention has the advantages of high accuracy and high efficiency. With the method adopted, defects of poor accuracy and low efficiency in the reconstruction of the three-dimensional entity model of a reservoir core in the prior art can be eliminated, and an effective guarantee can be provided for reservoir core simulation characteristic research.

The invention discloses a depth convolution network optical remote sensing image target detection method based on extended residual convolution, which solves the problems of low detection accuracy rate and high false alarm rate of optical remote sensing image plane and ship in the prior art. The implementation steps are as follows: constructing a test data set; construct training data set; constructing a target detection network based on extended residual convolution for extended feature receptive field,training target detection network based on extended residual convolution using training data set; using the trained target detection network based on extended residual convolution to detect the target from the test data set, outputting test results. The network constructed by the inventionis more suitable for target detection of an optical remote sensing image by using an expanded residual convolution module and feature fusion, and not only improves the accuracy of a common target, butalso obviously improves the accuracy of small target detection for the optical remote sensing image. The method is used for object detection in optical remote sensing images.

The invention discloses a mechanical product assembly workshop material calling and dispensing management system and method based on an MES system. The system comprises a planning module, a material management module, an andon calling calculating module and a visualization monitoring module. By employing the planning module and the material management module as configuration prerequisites, the andon calling calculating module generates a production line assembly station material calling request according to concrete steps, including calling for the required quantity of material kind and response; meanwhile, the retrieval of materials on that day is connected in real time with tracking assembly station material calling so as to perform better material management. The management system and method can timely and accurately generate material dispensing demands according to real production processes, and perform effective warehouse management on that day so as to create more scientific and effective material dispensing management environment, and to increase an enterprise material dispensing management level.

The invention relates to a vehicle behavior monitoring system based on radar and video group, and a realization method thereof. The system comprises multiple sets of monitoring terminal equipment, multiple sets of data communication equipment, data storage equipment, a background data processing server, a vehicle driving state display module and the like; the monitoring terminal equipment is composed of a millimeter wave radar, a high-definition camera, a front-end data processing unit module and a power control module. Compared with the prior art, the system disclosed by the invention realizes the advantage complementation of the millimeter wave radar data and the high-definition video image data on traffic data acquisition by deeply fusing the millimeter wave radar data and the high-definition video image data, so that the detected data type is more comprehensive, and the accuracy, reliability and robustness are further improved by steps, thereby being beneficial to providing technical support for the aspects of traffic control, safety early warning and the like.

The invention relates to a robot embedded computing terminal integrated with high precision navigation positioning and deep learning. The embedded computing terminal is characterized by comprising a core processing unit Jetson chip, an FPGA chip, an embedded ARM module and multiple types of sensors which are integrated, wherein the sensors comprise a GNSS satellite navigation module, an IMU inertial navigation module, a laser radar device and a camera, the core processing unit Jetson chip is connected with the camera and the laser radar device, the FPGA chip is connected with the GNSS satellite navigation module, the IMU inertial navigation module and the embedded ARM module, and the embedded ARM module is connected with a servo motor of a control robot. The embedded computing terminal isadvantaged in that realization of the real-time high-precision positioning result solution can be supported, and environment sensing and mission planning of the robot are realized.

The invention discloses a thermal power generating unit medium and long-term time sequence progressive power generation coordination algorithm considering wind power uncertainty. The algorithm comprises the following steps: connecting with data of a wind power farm to input wind power farm annual power generation and monthly power generation predicted values to a program; calculating a monthly power generation plan of each thermal power generating unit based on the data, the predicted value and monthly power generation predicted values of hydroelectric generating set annual power generation, the predicted value and monthly predicted values of cross-regional transmission electric quantity, and the annual predicted value and monthly predicted values of load; performing rolling correction on the monthly power generation scheme according to the monthly maximum and minimum power generation limitations of the unit and a maintenance plan and outputting the plan. The thermal power generating unit medium and long-term time sequence progressive power generation coordination algorithm considering the wind power uncertainty can overcome the defects such as high investment cost and poor plan accuracy in the prior art so as to realize the advantages of low investment cost and good plan accuracy.

The invention discloses an abnormal track analysis method based on an adjoint model. The method comprises the following steps that a video structuring system carries out face recognition to obtain face video data and preprocesses the face video data, then accompanying analysis mining is carried out on accompanying personnel through a frequent model mining algorithm to obtain accompanying relationship data and accompanying risk coefficients, and the accompanying relationship data and the accompanying risk coefficients are recorded as risk accompanying personnel when the accompanying risk coefficients are larger than accompanying personnel risk threshold values; accompanying track abnormity detection is performed on the accompanying relation data of the risk accompanying personnel through anunsupervised learning algorithm, a supervised learning model is trained through an automatic feature engineering algorithm based on the abnormal track of the risk accompanying personnel, and carryingout risk accompanying personnel abnormal track analysis. The method is based on the face video structured data, combines with the adjoint analysis model, analyzes the abnormal trajectory through theabnormal trajectory detection algorithm on the basis of considering the personnel adjoint relationship, and overcomes the limitation of poor accuracy and applicability of constructing the model only from the perspective of time and position.

The invention provides an automatic regulating device for liquid crystal display luminance, which comprises a liquid crystal display, a camera and a computer, wherein the liquid crystal display comprises a signal processing circuit, a backlight control circuit and a backlight unit; the camera acquires an environmental image and transmits image information to the computer; the computer analyzes luminance information of the image and sends a corresponding control signal; the signal processing circuit receives the control signal and sends a corresponding adjustment signal; and the backlight control circuit receives the adjustment signal and correspondingly adjusts luminance of the backlight unit according to the adjustment signal. The automatic regulating device for the liquid crystal display luminance has low cost, adjusts the liquid crystal display luminance accurately, and has little external interference.

The invention discloses a people stream analysis method based on binocular vision. The analysis method comprises steps of stereo image obtaining; image preprocessing; face detection; human body positioning; counting of visitors and visit time, gender identification and age estimation; finishing people stream analysis in a set visit region around an exhibition stand. The analysis method can be applied to all kinds of public exhibition places like museums and related information of visitors in front of all exhibition products in a public exhibition place is acquired and analyzed, so an objective of people stream counting and analysis is achieved; the analysis method is characterized by high real-time performance, high precision and high implementation efficiency; and the method facilitates actually knowing of action characteristics of visitors, and provides a reliable data basis for all kinds of analysis, thereby facilitating improvement of self service quality and level of the public exhibition place.

A wireless communication network testing data collection and analysis method based on an intelligent mobile phone is characterized in that an intelligent mobile phone of an ATAW (Any Time, Any Where) abnormal data three dimensional collection software based on 'client perception degree' is mounted to monitor existing wireless communication network data at real time; if a client finds abnormal situation of a wireless communication network, the client collects data, automatically triggers a GPS module of the client and compresses and encrypts abnormal information and abnormal data; then under the condition that the wireless network communication network is covered, the client transmits the abnormal information and the abnormal data to an analysis system via the wireless network; and if the wireless communication network can not be covered, the abnormal information and the abnormal data are stored in the client temporarily. The overall collection and analysis process is closely connected, abnormal conditions of the network is timely found, network optimization is realized, and the method is simple, precise, reliable and convenient and easy to be operated.

A bolt torque detection device is characterized in that a rack slides up and down in the vertical direction on a rack base, wherein the rack is supported by the rack base to rotate around a horizontal line, the rack is coaxially connected with a rotating sleeve, and a tension tester is in transmission connected with the rack and drives the rack to slide; a head chuck is placed in a head supporting base hole to limit rotation of the head chuck around the horizontal line and relative to the head supporting base; rotation of a bolt head around the horizontal line and relative to the head chuck is limited; rotation of a nut chuck around the horizontal line and relative to the rotating sleeve and translation of the nut chuck along the horizontal line are limited; rotation of a nut around the horizontal line and relative to the nut chuck is limited; the nut and the bolt head are the same in height; the head supporting base is provided with a back cap hole, the end of a back cap stretches into the head supporting base hole to abut against the head chuck and the bolt head of a bolt in the head chuck, and leftward movement of the bolt head along the horizontal line and rotation of the bolt head around the horizontal line are limited. The bolt torque detection device is practical, precise, intuitive, rapid, convenient to use and low in cost and price.

The invention discloses a magnetic particle chemiluminescence immune assay detection kit for a carcino-embryonic antigen and a detection method of the detection kit, belonging to the technical field of kits. The magnetic particle chemiluminescence immune assay detection kit for the carcino-embryonic antigen comprises a carcino-embryonic antigen calibration material, a magnetic bead covered by an anti-carcino-embryonic antigen antibody, an enzyme labeled monoclonal antibody, an enzyme acted chemiluminiscence substrate and a concentrated washing solution. The detection kit disclosed by the invention utilizes the advantages of magnetic separation and chemiluminiscence detection methods so that the detection process is simple, the operation is easy and the automation is convenient; meanwhile, the detection kit further has the properties of high sensitivity, good specificity, low detection limit, good stability and the like and the requirements of clinic auxiliary diagnosis or detection of various cancers are met.

The invention discloses a subjective question scoring model and scoring method based on deep learning BERT-CNN. The subjective question scoring model comprises a BERT conversion word vector matrix module, a CNN semantic feature extraction module, a similarity calculation module and a scoring module, wherein the BERT conversion word vector matrix module is used for converting an answer text into aword vector matrix, and then transmitting the word vector matrix of each answer to the CNN semantic feature extraction module; the CNN semantic feature extraction module is used for acquiring a semantic feature vector of the answer text, includes a convolution layer and a pooling layer, and is used for obtaining semantic feature vectors of the answer texts and then transmitting the signals to thesimilarity calculation module for calculating a similarity value between the answer text semantic feature vectors and transmitting the similarity value to the scoring module for determine the score ofthe answer text. The subjective question scoring model is applied to subjective question scoring, can effectively reduce the labor cost, and can solve the problems that due to the fact that only keyword matching is carried out in manual scoring, the scoring result is inaccurate, and scoring is not fair.

The invention relates to an intelligent configuration system and an intelligent configuration method for a wireless sensor network. The system comprises a sensor network module, a gateway module, an output module, an execution module and an application layer interface module. The system and the method solve problems of poor adaptability, poor reliability and relatively great control cost existing in a device and a method of a traditional sensor network. The system and the method for the wireless sensor network have advantages of intelligent performance, strong robustness, good versatility and small control cost.

The invention belongs to the field of marine applied acoustics. In order to provide a new artificial classification method for underwater acoustical signals with stable classification results, low workload, good subjectivity, accurate feature extraction, high classification accuracy, and good adaptability, the invention provides a whale and dolphin Click-like sound and traditional sonar signal classification method and device. The device includes three subsystems: a signal preprocessing subsystem, a signal time-frequency feature extraction subsystem, and a signal time-frequency feature classification subsystem. The signal preprocessing subsystem comprises: a hydrophone, a sound storage medium, a sound de-noising module and an endpoint detection module. The signal time-frequency feature extraction subsystem comprises a signal short-time Fourier transform preprocessing module, a signal time-frequency contour extraction module, a signal time-frequency contour curve fitting and signal time-frequency feature extraction module. The whale and dolphin Click-like sound and traditional sonar signal classification method and device are mainly applied to marine applied acoustics.

The invention discloses a method for segmenting a gray scale image based on the multi-objective particle swarm optimization algorithm, relates to the technical field of image processing, and mainly solves a problem that a traditional method is single in evaluation index, has high possibility of leading poor consistency of regions, and is disordered in edge. The method comprises the following steps: (1) extracting characteristics of a gray scale image to be segmented, namely selecting a gray scale of the image as data for a cluster to be clustered; (2) setting an operating parameter and initializing a particle swarm; (3) performing multi-objective clustering on the data combining the multi-objective particle swarm optimization algorithm to obtain a solution set approximate to Pareto; (4) selecting a solution with the largest PBM index from the solution set obtained in the step (3) as the optimal solution, namely a clustering result; (5) allocating a clustering mark to each pixel of the image to be segmented according to the clustering result obtained in the step (4) to obtain a segmenting result. The method has the advantages that the consistency of regions is good according to the segmenting result, complete information can be reserved, and the computing speed is quick, and can be used for image target identification.

This invention relates to a blood cell analysis method based on machine vision. Inputting the blood sample into a computer by a camera to convert the color image into the black-and-white and then the binary one and scale the binary image; calculating the area and taking count of the scaled image to obtain the number of RBC, WBC and platelet. This invention has high efficiency and accuracy with low cost.

The invention discloses a method for monitoring a step profiler in measuring the accuracy of chip groove depth, which reduces the delay on monitoring of the chip groove depth, improves the accuracy on monitoring of the groove depth and achieves the purpose of monitoring the step profiler in measuring the accuracy of the chip groove depth. The method comprises the following steps of: selecting a plurality of wafers, and determining the depth of a groove in a wafer chip area through measuring the depth of a groove in a wafer scribing channel area and the thickness of a hard mask layer by the step profiler in allusion to each wafer; and determining whether the accuracy of the groove depth measured by the step profiler reaches the requirements on the accuracy through analyzing the depth value of the groove in the chip area of the plurality of the wafers. By adopting the technical scheme, the time delay on monitoring of the chip depth can be shortened, the accuracy on controlling of the chip groove depth is improved, and the purpose of monitoring the step profiler in measuring the accuracy of the groove depth is realized.

The invention discloses a deep learning-based red light violation detection method, which relates to the field of traffic violation detection. The positions of lanes and traffic lights at a traffic light intersection are determined in advance, and the traffic lights are matched with the corresponding lanes. The condition at the traffic light intersection is photographed and recorded through a camera, a photo set transmitted by the camera later is processed centrally, and whether a vehicle has a red light violation behavior is judged based on the positions of the same vehicle under the same traffic light on different photo frames. Besides, as for an off-line vehicle that is unrecognizable due to a long distance, whether to be a known vehicle is recognized in a vehicle similarity comparisonmethod. Various algorithms and models are combined, and the novel traffic violation detection method meets the requirements of violation detection accuracy and flexibility by urban road traffic management.

The invention discloses a method for monitoring chip groove depth, which aims to solve the problems of longer time delay on monitoring of the chip groove depth and poor accuracy in the prior art. The method comprises the following steps of: generating a layer of protective film on the surface of a wafer; carrying out groove photoetching and corrosion on a chip area and a scribing channel area of the wafer, forming a first groove in the chip area, and forming a second groove used for detecting the depth of the first groove in the scribing channel area, wherein the polarity of a groove depth testing module is identical to the polarity of a groove photoetching layer in the chip area; measuring the depth d1 of the second groove by a step profiler; and monitoring the depth d of the first groove according to the d1 and the thickness d2 of the protective film. By adopting the technical scheme, the time delay on monitoring of the groove depth can be shortened, and the accuracy on controlling of the chip groove depth is improved.

The invention provides a lithium battery unit defect detection method. The main technical scheme is that images of the front side and reverse side of a lithium battery unit are acquired, a Tucker machine supporting method is applied, defected and non-defected lithium battery unit image sets which are preprocessed are trained to obtain weight tensor, core tensor and other parameters, then the unit images to be detected are judged by applying the Tucker machine supporting method according to the parameters obtained through training to complete defect classification, and accordingly detection results are obtained. The lithium battery unit defect detection method utilizes the machine, uses tensor type of weight parameters obtained through Tucker decomposition and can completely retain information and structures of image data, and classification accuracy is improved. The lithium battery unit defect detection method can be widely applied to image based defect detection.

The invention discloses an improved PCAnet-based SAR image classification method which mainly solves a problem that high classification computation complexity and low classification computation efficiency are caused by low filter initialization efficiency and slow renewal learning of in synthetic aperture radar high resolution SAR image classification processes via technologies of the prior art. The improved PCAnet-based SAR image classification method comprises the following steps: (1) a step of data reading, (2) a step of slicing, (3) a step of normalization pretreatment, (4) a step of extracting an image low frequency component, (5) a step of training a principle component analysis net PCAnet, (6) a step of obtaining characteristic vectors of a test set, (7) a step of classification accuracy calculation and (8) a step of outputting a classification result. The improved PCAnet-based SAR image classification method is advantaged by short SAR image classification time and high classification accuracy.

The invention discloses a data transmission method and a device, and a data processing method and a device. The data transmission method comprises the following steps that: a wireless frame control channel including one or a plurality of control signaling(s) occupying the same resource quantity is configured; and the data transmission is carried out on a wireless frame. Through the methods and the devices provided by the invention, the signaling expanse of the control channel is reduced, and the data transmission efficiency is improved, and the user communication quality is improved.