174results about How to "Improve discrimination accuracy" patented technology

The auxiliary safety operation equipment based on omnibearing computer vision includes an omnibearing vision sensor for acquiring omnibearing video information outside and inside the vehicle, and an auxiliary safety operation controller for detecting fatigue operation and warning on fatigue operation. The omnibearing vision sensor installed in the right of the driver's seat and connected to the auxiliary safety operation controller detects the facial state, eye state and mouth state of the driver and the steering wheel state, monitors the vehicle direction, speed, etc, and warns the fatigue operation in case of detecting fatigue operation state. The present invention detects the characteristic parameter of fatigue operation state, and has high judgment precision and high measurement precision.

The invention discloses a malicious traffic detection method. The method comprises the following steps: correspondingly establishing malicious and normal data sample libraries by using obtained malicious and normal data traffic samples; executing a data cleaning operation and a preprocessing operation on the data sample libraries in sequence to obtain training data, and constructing a traffic detection model by using the training data and a deep learning algorithm; judging whether to-be-measured data traffic contains malicious data by using the traffic detection model; and if so, sending alarminformation carrying the to-be-measured data traffic belonging to malicious data via a preset oath. Feature learning and training are performed by using the malicious and normal data traffic samplesvia the automatic learning property of the deep learning algorithm, the feature information extraction operation is completed without consuming precious human resources, thereby improving the improving the work efficiency and improving the discrimination of the malicious traffic. Precision. The invention further discloses a malicious traffic detection system and apparatus and a computer readable storage medium, which have the above beneficial effects.

The invention relates to the field of computer vision and discloses a computer vision-based vehicle damage distinguishing method so as to solve a problem that a manual distinguishing approach adopted in conventional technologies is incomplete, inaccurate and inefficient in distinguishing operation. The computer vision-based vehicle damage distinguishing method comprises the following steps: in step a, a binocular image collection system is calibrated; in step b, the binocular image collection system is used for collecting images in a monitored area, and a depth map for the collected images is obtained; in step c, a convolutional neutral network is used for subjecting the depth map to characteristic extraction training operation, and a vehicle damage condition discrimination model is trained; in step d, the vehicle damage condition discrimination model is used for determining a damage degree based on collected vehicle images. The computer vision-based vehicle damage distinguishing method is suitable for distinguishing vehicle damage.

The invention discloses a real-time dynamic judgment method and device for a road traffic state. The method includes the following steps: during a set period, GPS information transmitted by a set number of floating vehicles in a selected geographic area is acquired in a real-time manner in a data acquisition period; unreasonable GPS information is screened in the acquired GPS information; geographic position points, corresponding to geographic position information included in each piece of GPS information which is reserved after the screening, are projected respectively to road segments of an electronic map corresponding to a selected geographic area; and according to acquisition times and floating-vehicle identification information, which are corresponding to the geographic position points projected to each road segment, and the length and limit speed of a corresponding road segment, the traffic state of the corresponding road segment in the set period is determined. The real-time dynamic judgment method and device are capable of covering all road segments of a selected geographic area, large in applicable scope and high in judgment precision.

The invention discloses a document image classification method and device, and belongs to the field of computer vision. The method comprises: training a text feature vector extraction model and an image feature vector extraction model respectively, extracting a fusion feature vector of a document image in an embedded feature mode that text feature vectors and image feature vectors are fused, and classifying the document image based on the similarity of the fusion feature vector. According to the method, various document images can be quickly registered and classified, the service process can be greatly simplified, the OCR API can be simplified, all documents can be recognized through one API, and permanent use of one-time access is truly achieved.

The invention relates to a seafloor instability evaluation method. The method comprises: firstly, selecting out critical geological factors influencing seafloor slope stability according to geological environment conditions of seafloor slope occurrence and basic conditions of seafloor slope instability; secondly, selecting out critical induction factors influencing seafloor slope stability through a decomposition mode and a decomposition effect of natural gas hydrates, and constructing a seafloor slope stability influence factor index system; thirdly, constructing a seafloor slope geomechanics model by generalizing the seafloor slope stability influence factor index system; and finally, based on a Mohr-Coulomb failure criterion, proposing a mathematic method for evaluating seafloor slope stability, i.e., an improved finite element strength reduction method, and analyzing an evolutional process of seafloor slope stability and seafloor slope instability induced by decomposition of natural gas hydrates. The method is scientific in principle, high in calculation precision and short in calculation time, considers the induction factors of seafloor slope instability caused by decomposition of natural gas hydrates for the first time, gives out a deformation process of slope instability in combination with the geomechanics model and the finite element numerical analysis method, and has important practical application values.

The invention discloses a spam mail filtering system and method based on clusters. The system comprises a cluster module, a mail training module, a characteristic base storage module and a mail filtering module. The method includes that S1.1 training mails and unmarked mails are obtained from a mail backup system; S1.2 a cluster preprocessing module preprocesses the mails; S1.3 a cluster analyzing module divides the preprocessed mails into different clusters; S1.4 a cluster central computing module computes vector expression of the clusters; S1.5 the training module studies marked mails in the clusters and updates characteristic bases corresponding to the clusters; S2.1 mails required to be filtered are obtained from a mail system; S2.2 a mail class attribute distinguishing module calculates the cluster most similar to mail content; S2.3 a mail characteristic extraction module conducts characteristic extraction on the mails to be distinguished; S2.4 a mail distinguishing module shows a distinguishing result according to the mail characteristics and the corresponding characteristic bases. The system and method has the advantages of being high in extraction speed, high in accuracy and good in effect.

The invention belongs to the bridge monitoring technical field and provides a bridge structure modal parameter authenticity and falsity discrimination method and a terminal device. The method includesthe following steps that: the monitoring data of the structure of a bridge under an environmental excitation condition are obtained through data acquisition devices disposed in a plurality of measuring points of the bridge; the NExT (natural excitation technique) is adopted, at first, the cross power spectrum density function of the monitoring data is calculated, and Fourier inverse transformation is performed on the cross power spectrum density function, so that the cross-correlation function of the monitoring data is obtained, the cross-correlation function is adopted as the input data of amodal parameter identification algorithm, so that modal parameter identification can be carried out, and a stability diagram can be drawn; the auto-power spectrum density function and cross-power spectrum density function of the monitoring data of the measuring points are calculated, the auto-power spectra and cross-power spectra of all the measuring points are summated, and the auto-spectra andthe cross spectra are summated in the same order of magnitude, so that the sum function of the power spectra can be obtained; and a diagram of the sum function, and true and false modal parameters canbe discriminated according to the stability diagram of the sum function. According to the method, the stability diagram method and a power spectrum summation peak method are combined to perform bridge structure modal parameter authenticity and falsity discrimination, so that discrimination accuracy can be improved.

The invention discloses a cloud-computing oriented network intrusion detect method. The method comprises the steps of generating a pseudo-gradient promotion decision-making tree set for judging network intrusion behaviors; determining weight information of pseudo-gradient promotion decision-making trees according to classification features of non-leaf nodes corresponding to the pseudo-gradient promotion decision-making trees; carrying out parallel judgment on received network behavior records through adoption of the pseudo-gradient promotion decision-making trees, thereby obtaining independent judgment results; and multiplying the independent judgment results by the corresponding weight information, thereby generating final result information for judging whether the network behavior records are network attacks or not. The method and the system are characterized in simplicity, legibility, high distinguishing precision and high smart comprehensive processing capability. The pseudo-gradient promotion decision-making tree can be generated in a distributed parallel mode under a cloud computing environment. The dynamic update operation speed of the decision-making threes is improved, and the timeliness and accuracy of detecting new-type intrusion events by an IDS are improved.

The invention provides a new karst stage judgment method. The karst reservoir is a new field of recent oil and gas exploration and development, and the judgment of karst development stage is the difficulty in a karst reservoir modeling technology at home and abroad. The existing karst stage judgment method mainly takes an exposure marker as basis of judgment, but various karst process forms exist, so that the karst processes caused by factors such as hydrothermal solution are difficult to judge due to the lack of an exposure marker. The invention provides a new method for objectively judging the karst stage by combining a method for judging the filling stage by carbon and oxygen isotopes of karst fillings and a method for judging the karst fluid active stage by inclusion homogenization temperature aiming at the problem of karst stage judgment. A carbon and oxygen isotope discrimination chart and an inclusion homogenization temperature comparison diagram are established by the new karst stage judgment method, so that the karst development stage can be accurately and rapidly judged, the limitation that the traditional karst stage judgment takes the exposure marker as basis is broken through, and the problem that karst stage judgment is difficult under the condition that the underground deep layer is short of exposure marker can be solved.

The invention discloses a safety factor calculation method for seabed slope stability. The safety factor calculation method comprises the steps of acquiring and calculating wave characteristic parameters and seabed soil mass strength parameters of a sea area; establishing a numerical model based on the wave power characteristics and the soil body strength parameters; analyzing the wave field dynamic model and the seabed slope numerical model based on numerical wave generation and a finite element strength reduction method to obtain a slope stability safety coefficient; and acquiring stabilitychange analysis and estimation under a certain slope rate based on different relative positions of waves and slopes. The stability coefficient of the underwater side slope under the wave load can be estimated, and convenience is provided for selection of the construction slope rate of the underwater temporary side slope. The optimized design slope rate is obtained on the basis of different designparameters and calculation working conditions by utilizing the convenience of numerical simulation calculation. When certain safety requirements are met, the earth excavation and backfill amount of the whole foundation trench is reduced, and damage to the seabed natural ecological environment is reduced to a certain extent.

The invention provides a sleeping posture detection method; when human body has different sleeping postures, a neck back and a shoulder can generate different pressure or pressure intensity on a human body support device; the method can realize detection distinguishing; a specific representation: when people lay on his/her back, the human body neck back, the human body support device and a head support device can form a natural cavity, and the human body support device in the cavity can receive very small pressure or pressure intensity from the neck back; when people lay on his/her side, the shoulder falls into the cavity, so the human body support device in the cavity can receive obviously bigger pressure or pressure intensity from the shoulder. The sleeping posture detection method is high in detection precision, is not limited by body weight, height, and sexes, and has excellent suitability; the invention also provides a pillow with an adjustable height, and the pillow is designed according to the said sleeping posture detection method.

The invention discloses a wrong-pedaling-prevention protection method for an accelerator pedal of a novel battery electric vehicle. The method is characterized by comprising the steps that a sensor obtains the driving state and before-vehicle obstacle information of the vehicle, and transmits the state and information to a signal analysis and processing system through a CAN, the processing systemmakes a judgment and finally transmits the judgment result to a finished vehicle controller through the CAN, and the finished vehicle controller sends out a final instruction; a radar sensor is adopted for collecting information of the distance between an obstacle in front of the vehicle and the vehicle; meanwhile, a vehicle speed sensor collects vehicle speed information, the finished vehicle controller records the opening degree and time of the accelerator pedal in real time, and through derivation, the change rate of the accelerator pedal is obtained; moreover, a wrong-pedaling-prevention protection system for the accelerator pedal of the novel battery electric vehicle can preset threshold values of three parameters of the vehicle distance, the vehicle speed and the change rate of the accelerator pedal to serve as safe driving critical values. The method has the excellent advantages that the distinguishing accuracy is very high, the system stability is high, redundant cost is not added, and the probability of the occurrence of traffic accidents is lowered.

An image analysis method for generating data indicating a tumorigenic state of an image of a tissue or a cell. The image analysis method is an image analysis method for analyzing an image of a tissue or a cell using a deep learning algorithm of a neural network structure, analysis data are generated from the analysis target image including a tissue or cell to be analyzed, the analysis data are input to the deep learning algorithm, and data indicating the tumorigenic state of tissues or cells in the analysis target image are generated by the depth learning algorithm.

The invention discloses an aero-engine gas circuit fault diagnosis method based on deep learning and information fusion, and belongs to the field of power machinery fault diagnosis. The method comprises the following steps: preprocessing data; secondly, respectively training a convolutional neural network model and a feedforward neural network to be optimal by utilizing the processed sample data,so as to obtain judgment results of the two models on the category to which the sample data belongs; thirdly, in the construction stage of the basic probability distribution of the evidence bodies, judging results of the two models are regarded as the two evidence bodies, in the stage, scores of all categories to which the samples in the evidence bodies belong are converted into the basic probability distribution of the samples, and meanwhile the uncertainty of the samples is calculated; and finally, in the evidence body synthesis and decision-making stage, calculating probability distributionof the two evidence bodies after synthesis according to a synthesis rule of a D-S evidence theory, and giving a category to which each sample belongs according to a decision-making rule. According tothe invention, decision results given by two deep neural networks after learning sample data from different angles are fused, an aerial fault diagnosis model with high precision is obtained, and theproblems of unstable discrimination capability and low robustness of a non-deep learning model for aerial big data from different sources can be overcome.

The invention discloses an abnormal data discrimination method based on multi-criterion fusion. The method comprises the following steps: S1, preprocessing a sample data set; S2, according to four detection models in the abnormal data discrimination model, establishing sample data sets corresponding to the four detection models respectively; S3, inputting the input data set of the deep learning method into the long-short-term neural network model for training, and obtaining the trained long-short-term neural network model; S4, respectively inputting the sample data sets into the correspondingmodels, discriminating abnormal values, and obtaining the probability that each data point is discriminated as an abnormal point by the corresponding model; and S5, fusing the discrimination result probabilities of the data points discriminated by the corresponding models, and judging a fusion result according to a set judgment criterion to obtain a final abnormal data discrimination result. The data abnormal point discrimination precision is improved, the data accuracy and the available value are also improved, and accurate data guarantee is provided for operation detection business.

The invention relates to a rotary kiln firing state recognition method based on flame picture structure similarity. According to the technical scheme, the method includes the steps that a standard flame gray level image y, a normal flame gray level image storage FN and an abnormal flame gray level image storage FA are set; a to-be-monitored flame image P is obtained, and filtering and grey level transformation are carried out on the to-be-monitored flame image P to obtain a to-be-monitored flame gray level image x; average structural similarity coefficient calculation is carried out on the to-be-monitored flame gray level image x and all standard flame gray level images to obtain a+b MSSIM coefficients (x,y); the maximum value MAXssim of the MSSIM coefficients is selected, if the standard flame gray level image y corresponding to the maximum value MAXssim of the MSSIM coefficients belongs to the normal flame gray level image storage FN, the to-be-detected flame image P belongs to a normal state, and otherwise to-be-monitored flame image P belongs to an abnormal state. The method has the advantages of being high in precision, low in computation complexity, short in processing procedure and capable of achieving online real-time monitoring of flame state changes.

Provided is a geo-environmental simulation and prediction platform for coupled geo-environmental elements. The platform comprises an infrastructure layer, a data layer, a supporting layer, an application layer and a presentation layer; the application layer comprises a basic module, a data management module, a data mining module, a subject analysis module, a regional analysis module and a compressive assessment module, wherein the basic module is used for serving as basic auxiliary of the simulation and prediction of typical regions; the data management module is used for the simulation of typical regional elements; the data mining module performs ground-object recognition, change detection, trajectory analysis, hot point detection and visualized analysis; the subject analysis module conducts geopolitical pattern analysis, geopolitical conflict analysis and geopolitical relation analysis; the regional analysis module analyzes specific regions; and the comprehensive assessment module conducts terrorist attack risk evaluation, armed conflict risk evaluation and geo-environmental risk comprehensive evaluation. The geo-environmental simulation and prediction platform for the coupled geo-environmental elements provides a set of visualized and visual analysis platforms for geo-environmental researchers, decision-makers and managers, and can assist the researchers in analysis and decision-making.

The method includes steps: first, preparing histogram statistics for mismatched pixels of moving forwards compensation block in current input image; next, calculating statistic of heterotactic characters of histogram for mismatched pixels of moving forwards compensation block between adjacent frames; then, carrying out determining procedure for switching scenes based on the said statistic, and outputting the determined result. Based on difference detection technique of moving compensation, the invention overcomes disadvantages: sensitivity to noise and movement caused by the difference algorithm for pixels located at same positions in adjacent frames; and negligence of space information of video object occurred in conventional decision method of analyzing statistical histogram of pixel intensities. Advantages are: lowering probability of false alarm and probability failed to report in detection of switching video scenes as well as raising precision and correctness of determination.

The invention discloses a special infrared reflection type yarn guide device for yarn breakage monitoring. The special infrared reflection type yarn guide device is characterized in that the upper lateral side, the lower lateral side and the periphery of a spinning frame yarn guide plate are provided with an infrared reflection type monitoring device respectively; the continuity of yarn production in the process of spun yarn spinning is sensed by every monitoring device, the intensity of a reflected light analog signal is converted into a digital signal, and the analysis is performed on the output digital signal so as to determine whether spun yarns of a ring spinning frame are broke yarns or not; the judgment on the yarn breakage of the spinning frame is based on whether the digital signal is produced or not within a certain period of time, the spun yarns are not broken if yes, and the spun yarns are broken otherwise. The special infrared reflection type yarn guide device for the yarn breakage monitoring is simple in structure, free of special requirement for raw materials and the yarn variety and applicable to the yarn breakage monitoring of the ring spinning of cotton spinning, wool spinning and color spinning and the like.

The invention discloses an S1-MME data-based method of determining mobile user rapidly and accurately, belonging to the technical field of data analysis. According to the method, a pre-determined useris screened out by collecting S1-MME interface data in an LTE mobile communication network, and a practical traffic route and a travel distance of the user are simulated and calculated by combining an algorithm calculating a projection of a base station cell set covering the traffic route on a geographic traffic route and a cell coverage track segmentation algorithm. The method replaces a mannerof calculating a user movement speed by using cell space in the prior art, so that the movement distance and speed are determined more accurately; and the user is determined by using both a maximum span determining method and a reverse slide window determining method, so that the determining accuracy is improved, manual intervention factors are reduced, and false negative and false positive problems resulting from the fact that fewer cells cover the traffic route or traffic routes of different mobile attributes are adjacent are solved.