322 results about "Probability model" patented technology

The invention relates to the technical field of electronic component detection, in particular to an IC (integrated circuit) carrier plate detection method based on surface state image extraction, which comprises the following steps: acquiring a gray image, analyzing structural parameters, extracting gradient features, detecting boundary disturbance, integrating the image, calculating an abnormal score, identifying a defect position area, extracting features and outputting an identification result. According to the invention, by analyzing the structure parameters of the bonding pad in the gray level image, calculating the edge line segment, the center coordinate and the spacing, and constructing the two-dimensional coordinate system, the regional positioning reference is enabled to have geometric consistency, the coordinate mapping is combined with the gradient direction change frequency and the continuous aggregation point, the boundary disturbance identification precision is improved, and the image division is executed based on the disturbance region. According to the method, non-functional region mixing is effectively avoided, a clustering and probability model is introduced after region gray level statistics, a deviation scoring mechanism is constructed, gray level feature abnormity is accurately recognized, the discrimination capability of small-amplitude and low-contrast defects is improved, and the selectivity and target focusing performance of feature detection are enhanced.

The invention discloses a human body behavior prediction method and system, and the method comprises the steps: carrying out the skeleton point sequence extraction of a real-time behavior video image of a target person, and obtaining a joint point coordinate set and a skeleton motion sequence; determining a spatio-temporal feature sequence of the joint point coordinate set, and determining a skeleton motion sequence to perform spatio-temporal attention coding to obtain global spatio-temporal dynamic features; fusing the action probability distributions corresponding to the spatial-temporal feature sequence and the global spatial-temporal dynamic features to obtain short-time action probability distribution data; candidate action screening is carried out on the short-time action probability distribution data, and candidate action comprehensive features are obtained; performing feature coding on the historical action sequence of the target person to obtain a historical context vector, and splicing the historical context vector with the candidate action comprehensive features to obtain a fusion feature; and inputting the fusion features into a probability model for intention probability evaluation to obtain a behavior prediction result of the target person. According to the method, the accuracy of behavior prediction is improved.

The invention relates to the technical field of intelligent security and protection, in particular to a public area video monitoring system and method, and the system comprises an intelligent collection and recognition module, a feature fusion module, a heterogeneous trajectory prediction module, a dynamic risk field module, a hierarchical response module and a collaborative warning module. The feature fusion module integrates video data and real-time weather and traffic flow information, and constructs a multi-dimensional environment state vector, the heterogeneous trajectory prediction module adopts Social-LSTM, TransMotion and random field probability models to process motion characteristic differences of three types of targets respectively, the prediction precision is significantly improved, and the dynamic risk field module calculates the trajectory intersection probability, so that the prediction efficiency is improved. According to the method, the environmental dynamic factors and the object danger level are weighted and integrated, dynamic quantification of space-time risks is achieved, the problem of prediction errors caused by motion characteristic differences is solved, the defect of risk misjudgment influenced by environmental factor changes is overcome, and accuracy and practicability are higher.

The invention relates to the technical field of vehicle intelligent management, and discloses a vehicle state monitoring method and system based on deep learning, and the method comprises the steps: analyzing the physical dependence relation among vibration abnormality detection, loosening positioning and fatigue evaluation monitoring tasks based on structural mechanics constraints, and carrying out the monitoring of the vibration abnormality detection, the loosening positioning and the fatigue evaluation; generating a task dependence directed graph; inputting the multi-modal monitoring data matrix into a shared encoder of a multi-task learning network to generate a unified representation vector; respectively inputting the unified representation vector into a vibration anomaly detection decoder, a loose positioning decoder and a fatigue evaluation decoder, and outputting an initial prediction result of each task; and establishing a conditional probability model between task outputs based on the constraint relationship defined by the task dependent directed graph, and generating a consistency diagnosis result meeting physical constraints through maximum posteriori estimation. The technical problems of one-sided diagnosis results, mutual contradiction and low calculation efficiency are solved.

The invention discloses a Lidar point cloud registration optimization method based on geometric feature reliability, and the method comprises the steps: carrying out the preprocessing and initial alignment of a laser radar scanning point cloud, extracting the features of a maximum principal curvature and a minimum principal curvature based on the local curvature of the point cloud, dividing a point region into two types of geometric features of angular points and plane points according to the threshold value of the maximum principal curvature, and carrying out the registration of the angular points and the plane points. On the basis, fitting quality factors including fitting errors, local curvatures and spectral entropies of the linear features and the plane features are calculated respectively, then the three factors are fused into a unified reliability weight based on a Bayesian probability model, and finally the feature reliability weight is introduced into an optimization objective function of iterative nearest point registration to execute weighted ICP registration. And outputting positioning and attitude determination results. According to the method, the reliability of geometric features is quantitatively evaluated, and a weighted optimization framework is constructed, so that the point cloud registration precision and robustness are remarkably improved, and the problem that a traditional ICP algorithm is sensitive to unreliable features in feature degradation or high-dynamic scenes is effectively solved.

The invention discloses an underwater three-dimensional reconstruction and sonar pose joint optimization method based on a multidirectional sonar, and belongs to the technical field of image reconstruction, and the method comprises the steps: obtaining and preprocessing multi-modal sensor data, and converting coordinates into a world coordinate system; dead reckoning is carried out through the IMU and the DVL to obtain a rough pose; constructing and optimizing an implicit occupancy probability model; screening effective frame pairs; calculating point cloud loss; a total loss function is constructed, the pose and the model parameters are jointly optimized, and a reconstruction result and an optimization track are output; according to the underwater three-dimensional reconstruction and sonar pose joint optimization method based on the multidirectional sonar, the reconstruction problem caused by lack of features and unstable sensor data in a turbid water area is effectively solved.

The invention provides a steel plate surface defect image generation method based on a de-noising diffusion probability model and a visual Transform, relates to the technical field of industrial surface defect detection, and aims to solve the problems of low quality of generated samples, lack of detail richness and insufficient global dependency relationship modeling capability in the conventional defect image generation method. The performance improvement of a defect detection algorithm is limited; according to the generation method provided by the invention, by introducing the ViT model, the capability of capturing the global dependency relationship in the image by the model is remarkably enhanced, the limitation of the traditional DDPM in modeling the global structure of the image is overcome, the diversity and quality of the generated image are effectively improved, and the method is suitable for further improving the modeling precision of a complex defect structure. A channel-space self-attention module is designed, and the CSSAM of the module can dynamically adjust the attention between different space regions and channels, so that the details of complex defects are finely simulated, and the authenticity and high fidelity of a generated image on the aspects of a global structure and local textures are ensured.

A power consumption prediction and management method and system based on a solid state disk, a medium and a product relate to the field of solid state disks. The method comprises the following steps: predicting the workload intensity of a host in a first preset time window based on an I / O (Input / Output) instruction stream to obtain an expected host workload curve, and determining an expected host idle time period; calculating an expected host power consumption value in the expected host idle time period; extracting internal state parameters of the solid state disk according to the historical operation log data; establishing a background task triggering probability model, and calculating the triggering probability of starting the high-power-consumption background task by the solid state disk in a first preset time window; performing power consumption conflict judgment according to the expected host power consumption value and the triggering probability, and generating a comprehensive predicted power consumption value; and based on the comprehensive predicted power consumption value, determining an optimal target power consumption state, and instructing the solid state disk to be switched to the optimal target power consumption state. By implementing the technical scheme provided by the invention, the stability of state switching of the solid state disk is improved.

The invention provides a big data compliance risk control method and system, and relates to the technical field of big data processing and risk control. By obtaining the original business data and adding the metadata information for packaging, standardization and traceability of the data source are realized. Then, the metadata information is utilized to carry out preliminary matching in the data mode dictionary, and the potential mode of the data can be quickly identified. The data structure and the field type are inferred through the probability model, and the inference confidence is output, so that the problems of analysis errors and label noise caused by nonstandard data format and update lag of the analyzer in the prior art are effectively solved.

The invention discloses a power transmission line microtopography icing risk early warning method and system, and relates to the technical field of power transmission line monitoring, and the method comprises the steps: dividing a power transmission line region, collecting multi-source data, carrying out the processing, analyzing the related parameters of a power transmission line, analyzing the influence of the microtopography on the power transmission line, and extracting the icing features of the data; the method comprises the following steps: constructing an icing probability model and an icing severity model by adopting XGBoost, optimizing a training weight and calculating a calibration score through historical icing data, calculating the calibration score in combination with a weighted PAV fitting non-drop function, carrying out optimization training on the icing probability model, and calculating a load utilization rate based on the icing severity to judge the risk of the power transmission line. According to the method, the icing feature system is constructed through fusion of multi-scale terrain and meteorological factors, the power transmission line is analyzed from the micro-terrain scale, an equivalent regression calibration mechanism based on verification external prediction is introduced, the icing probability model is optimized, and the credibility and stability of a prediction result are remarkably improved.

The invention discloses an intelligent flood forecasting method for coupling error correction and joint modeling, and relates to a deep learning and uncertainty modeling technology. At the input end, constructing a future random rainfall scene through hourly dynamic normal disturbance; the method comprises the following steps: at a model end, introducing a multi-structure and multi-objective function combination based on Kolmogorov-Arnold Networks and Transform, and forming a multi-member ensemble forecast; at an error end, a probabilistic modeling method based on a numerable asymmetric Laplacian mixed density network is provided, and fine error correction is realized; a Vine copula function is adopted to construct high-dimensional joint distribution, and a Bayesian model averaging and expectation maximization algorithm is combined to realize weighted fusion of multi-member posterior results; according to the method, uncertainty in flood forecasting can be comprehensively described, the stability and adaptability of a forecasting system are improved, and the method is suitable for a basin-level real-time flood ensemble forecasting scene.

The invention discloses an air duct vibration on-line monitoring system based on multiple sensors and a fault prediction method, and belongs to the technical field of air duct state monitoring and fault prediction.The method specifically comprises the steps that air duct vibration acceleration and noise signals are collected in real time through the sensors, and a time-aligned signal sequence is formed through analog-to-digital conversion and preprocessing; extracting a time-frequency domain feature from the vibration signal sequence, extracting a sound pressure level and a harmonic distortion degree from the noise signal sequence, and generating an air duct feature vector through feature fusion; constructing a Bayesian probability model based on the vector, calculating a fault occurrence probability, judging existence and generating a confidence score; calculating the sliding sample entropy of the vibration signal sequence, and marking an entropy value abnormal interval; and a union set of a fault interval diagnosed by the Bayesian probability model and an entropy abnormal interval is obtained, after time sequence trend analysis, early warning information is generated and pushed to an operation and maintenance platform when a joint judgment condition is met, and online updating of a normal vibration entropy model is triggered, so that the fault detection and early warning precision is improved.

The invention relates to the technical field of monitoring, in particular to an intelligent camera monitoring data storage and data enhancement processing method, which effectively solves the image quality problem under a complex illumination condition through the combination of illumination invariant feature extraction and a weighted Gaussian probability model, especially the influence of dynamic shadow, uneven illumination and the like. According to the dynamic shielding synthesis mechanism, the dynamic shielding in a real scene is simulated through a Poisson fusion method based on a semantic segmentation result and a physically reasonable shielding object generation algorithm, the limitation of a conventional fixed template shielding method is avoided, the diversity and spatial rationality of shielding objects can be better reflected, and the real-time performance of the real scene is improved. The robustness of the model to shielding is improved; according to the invention, through combination of the cross-scale residual aggregation module and the gating channel-space attention mechanism, multi-scale feature reservation and deep fusion are realized, and detail information under small targets and complex illumination can be effectively captured in a complex monitoring scene.

The invention discloses a foundation pit horizontal displacement probability prediction method and system based on a sparse Bayesian extreme learning machine, and belongs to the technical field of civil engineering monitoring and artificial intelligence crossing. According to the method, a probability model containing input and output noise is constructed, feature selection and uncertainty quantification are automatically carried out by using a sparse Bayesian framework, and probability prediction of horizontal displacement at the position where a sensor is not arranged is realized. The method can output the prediction mean value and the confidence interval, effectively solves the problems of data sparsity and noise, and improves the prediction reliability and the engineering decision support capability. The method has the advantages of being high in automation degree, high in anti-interference capacity, suitable for actual engineering monitoring and the like.

The invention relates to a gas leakage probability dynamic evaluation method and device based on a Bayesian network, and the method comprises the steps: constructing a gas leakage risk accident tree based on the historical data of gas leakage, and constructing a Bayesian network evaluation model of the gas leakage risk based on the gas leakage risk accident tree; calculating a prior probability of each node in the Bayesian network evaluation model according to statistical analysis of gas leakage historical data; and based on the prior probability, reversely deducing the posterior probability of each node in the Bayesian network evaluation model so as to identify key risk factors having influences on the gas leakage risk, and obtaining a gas leakage probability dynamic evaluation result. Therefore, the problems that the related technology depends on a static probability model, lacks dynamic modeling ability and is difficult to accurately reflect risk evolution and dominant factors under multi-factor coupling, so that the evaluation result is lagged, and the overall evaluation ability is limited are solved.

The invention relates to the technical field of power distribution networks of power systems, in particular to a power distribution network elasticity improvement method based on distributed dynamic recovery, which comprises the following steps: step 1, acquiring operation data of a power distribution network; 2, establishing a topology reconstruction and rolling optimization dual-stage recovery model, taking maximized load recovery as a target, considering network constraints and resource operation constraints, and converting a probability model into a deterministic mixed integer linear programming problem through opportunity constraint conversion; 3, decomposing the model into distributed sub-problems by adopting an alternating direction multiplier method, introducing a boundary variable compensation mechanism to enhance the robustness under communication interruption, and realizing a multi-period dynamic recovery decision based on rolling optimization; step 4; an integer variable processing method combining projection and relaxation iteration is designed, and an induced acceleration objective function is constructed, so that the solving efficiency of a mixed integer sub-problem is improved, and the convergence and calculation speed of a distributed algorithm are ensured.

The invention discloses an obstacle motion state identification method, device and equipment, and a computer program product, and the method comprises the steps: obtaining continuous multi-frame point cloud data collected by a vehicle-mounted laser radar, the continuous multi-frame point cloud data comprising current frame point cloud data and historical frame point cloud data; performing motion compensation and conversion superposition on the continuous multi-frame point cloud data to obtain multi-frame point cloud data under the current frame coordinate system; extracting a spatial position feature sequence of an obstacle from the multi-frame point cloud data under the current frame coordinate system; according to the spatial position feature sequence of the obstacle, a preset clustering algorithm and a preset probability model are used for recognizing the motion state of the obstacle, and the motion state is divided into a dynamic state and a static state. According to the method, a dynamic and static discrimination mechanism fusing multi-frame point cloud data and probability modeling is adopted, the accuracy of obstacle motion state recognition of the automatic driving vehicle is improved, the robustness in noise, shielding and other complex scenes is enhanced, and the safety and comfort of an automatic driving system are improved.

The invention relates to the technical field of data compression, in particular to a data compression method and system, which comprises an LZ module, a text stream module, a sequence stream module and an interval entropy coding framework, and is characterized in that the LZ module is used for performing repeated data screening on original data to generate a text stream and a sequence stream; the LZ module screens duplicated data through a hash verification method, specifically, hash values are calculated for input data blocks, the duplicated data are determined based on hash value comparison, and direct data content comparison is avoided; and the character flow module is used for carrying out entropy coding on the character flow. According to the method, an interval entropy coding framework is innovatively proposed, a better probability model is constructed through artificial intelligence and a mathematical method, a better finite state machine is trained, the optimal state machine is used in the compression and decompression process, and the problem that the decompression speed of a neural network method is low is efficiently solved.

A transformer area photovoltaic consumption capability assessment method considering source load randomness relates to the technical field of photovoltaic power generation, and comprises the steps of collecting transformer area topology, line impedance and other data in real time through a multi-dimensional perception layer, extracting and quantifying the source load randomness by using dynamic features, constructing a self-adaptive probability model, and assessing risks in combination with an extreme value theory. A photovoltaic access capacity strategy is optimized by applying reinforcement learning, and the feasibility of the scheme is ensured through physical-random consistency verification, so that the photovoltaic consumption capability is finally improved, and safe and stable operation of a power grid is ensured. According to the method, multi-dimensional perception, dynamic feature extraction, adaptive modeling and reinforcement learning optimization are fused, the photovoltaic consumption capability evaluation precision and the power grid operation stability are remarkably improved, and powerful support is provided for high-proportion renewable energy source access.

This invention discloses an intelligent control system for a space-type atomic layer deposition equipment, belonging to the field of intelligent control technology. The system of this invention includes: an acquisition module that acquires the pressure difference and flow rate values ​​of the gas curtain pipeline and performs low-pass filtering to obtain a pure signal matrix; a feature extraction module that calculates the flow field impedance and impedance residual based on this matrix and converts them into an empirical probability distribution vector; a model building module that constructs a degenerate distribution uncertainty set and a scheduling objective cost function based on this vector, and converts them into a mixed integer second-order cone programming model; an offline solution module that discretizes and classifies the dynamic distribution divergence tolerance and solves the model, and pre-generates a scheduling strategy library; and an online control module that looks up tables in the strategy library according to the real-time divergence tolerance and outputs substrate transmission speed control and channel switching commands. This invention transforms the minimization-maximization probability model into a convex optimization problem for offline solution, breaking the computing power bottleneck and balancing control response speed and scheduling decision-making through efficient table lookup.

The present application relates to the technical field of data compression, in particular to a data compression method and system, comprising: an LZ module, a text stream module, a sequence stream module, an interval entropy encoding framework, the LZ module is used for screening repeated data of original data, generating a text stream and a sequence stream; the LZ module screens repeated data through a hash verification method, specifically: calculating a hash value for the input data block, determining repeated data based on hash value comparison to avoid direct data content comparison; the text stream module is used for entropy encoding the text stream; the present application innovatively proposes an interval entropy encoding framework, constructs a more optimal probability model through artificial intelligence and mathematical methods, trains a more optimal finite state machine, and uses the optimal state machine in the compression and decompression process, efficiently solving the problem of slow decompression speed of neural network type methods.

The invention discloses an expected distribution coefficient calculation and power flow section generation method and system under new energy random fluctuation, and the method comprises the steps: obtaining line power flow data based on the operation state of a power system; presetting a new energy output probability model, and obtaining output data of new energy; on the basis of output conditions of different fluctuation degrees of the new energy, updating line power flows under different fluctuation degrees; and generating section power flow control of the N-K fault of the power system under the new energy fluctuation based on the updated line power flow data. According to the method, the new energy fluctuation factor is brought into the consideration range of the power flow transfer characteristic analysis under the N-K fault of the power system, so that the key section can be controlled under the real fault scene of the power system; through the influence on the distribution coefficient calculation fault load flow precision under the new energy fluctuation, an effective means can be provided for power grid workers to timely know the operation of a power grid, the accuracy of load flow calculation is improved, and the capacity of the power grid for coping with a complex fluctuation environment is also improved.

The application relates to the technical field of intelligent auxiliary driving, and provides a vehicle turn signal self-starting method and system.A fusion algorithm, such as a Bayesian network and a Kalman filter, is built in a steering decision model based on a deep learning algorithm.When line-of-sight features, steering wheel features and vehicle speed features are extracted, the reliability and correlation of different data are considered, the probability relationship between various factors is considered, the weights of the factors in a final result are dynamically adjusted, different steering intentions of a user are represented and predicted through a probability model, more reliable prediction results are provided, the accuracy and reliability of steering intention recognition are improved, the turn signal can be automatically turned on according to the line-of-sight direction intention of the driver through the cooperative work of a driver monitoring system and rotation monitoring (vehicle speed data and steering wheel data), the convenience and automation level of operation are improved, traffic accidents caused by improper operation of the turn signal are reduced, and road traffic safety is ensured.

One or more methods, apparatuses, computer-readable storage mediums, and systems for entropy coding vertex information of an edge in a voxelized space of a point cloud are disclosed. Symbols of a neighborhood configuration of a current edge may be determined based on one or more already coded edges. The already coded edges may be selected from a spatial topology of edges. The use of a motion-compensated point cloud for coding centroid residual values may enhance interframe correlation used for determining a context or probability model. This increased correlation may improve the selection of coders, leading to enhanced compression of centroid residual values.

The invention discloses a multi-source error self-correction ultra-high temperature colorimetric temperature measurement method, which comprises the following steps: firstly, carrying out RGB image acquisition on a calibration workpiece at a known temperature by using a blackbody furnace to obtain a calibration image, carrying out parameter calibration, establishing a colorimetric temperature measurement formula, and then determining a low-error characteristic curve according to the green channel characteristic of the calibration image; therefore, a probability model combining calibration data priori knowledge and actual observation data is constructed in an actual test link, and finally iterative optimization solution is performed according to the constructed objective function, so that errors of various sources are uniformly corrected, corrected colorimetric temperature measurement is obtained, the accuracy of high-temperature measurement is remarkably improved, and the accuracy of high-temperature measurement is improved. The method is suitable for accurate measurement of non-contact temperature in various ultra-high temperature environments.

The embodiment of the invention discloses an extended target detection method, belongs to the technical field of radar detection, and solves the problem that the detection performance is reduced due to the fact that excessive noise is introduced or target energy is omitted during energy accumulation in an existing extended target detection method. Comprising the following steps: constructing a dynamic planning accumulation detection model of a distance extension target based on an echo matrix; on the basis of the minimum energy loss criterion, balancing parameters are configured for the value function, a double-probability joint constraint optimization model about the target energy and the noise energy is established, and the balancing parameters are determined by solving the optimization model; updating a value function and a state transition matrix of the joint state variable among the plurality of pulses through recursive operation based on the trade-off parameter; and for the value function obtained after updating is completed, extracting a corresponding energy ridge line, constructing an approximate probability model of the variation amplitude of the energy ridge line, and obtaining a target extension length value by solving an optimal variation threshold of the approximate probability model under a preset false alarm probability.

The embodiment of the invention provides an iterative coupling method and system for equivalent environment parameters and structure parameters, and the method comprises the steps: S2, obtaining the monitoring data of a target structure, and building a complete machine model and a structure model of the target structure; s4, determining a probability model of the environmental data, simulating the whole machine model, and calculating initial equivalent environmental load parameters; s6, performing parameter updating on the second numerical model; s8, updating the first numerical model; s10, whether the first numerical model meets convergence conditions or not is judged, if yes, the step S12 is executed, if not, the first numerical model is used for iteration and equivalent environment load parameter calculation, convergence judgment is conducted, and the convergence conditions comprise the steps that the equivalent environment load parameters are calculated based on the first numerical model, and whether convergence is conducted or not is judged by comparing the result of the last time; and S12, final equivalent environment load parameters after convergence are determined, and safety evaluation is carried out on the target structure.

The invention belongs to the technical field of structural wind engineering, and particularly relates to an expanded circular line type Copula method for wind speed and wind direction joint probability modeling, which introduces additional degree-of-freedom parameters on the basis of a traditional circular line type Copula joint probability model, expands the application range of a traditional circular line type Copula function, and improves the modeling efficiency. And the precision of joint probability distribution modeling between a circular variable (wind direction angle) and a linear variable (wind speed) is improved. According to the method provided by the invention, during modeling, not only can periodic probability correlation characteristics between circular linear variables of the wind direction angle and the wind speed be captured, but also the joint probability distribution modeling precision of the wind speed and the wind direction angle can be remarkably improved by expanding the degree of freedom of the model; and a key theoretical and technical support is provided for wind load probability modeling analysis and structure fine design of a wind sensitive structure.

A mechanical arm grabbing attitude estimation method based on a de-noising diffusion probability model aims at the problems that a traditional grabbing method outputs a single pose and does not have the generation capacity, the de-noising diffusion probability model is adopted for modeling probability distribution of grabbing attitudes, and multiple grabbing attitude candidates with high quality and reasonable structures can be generated in the reasoning stage; and the grabbing diversity and robustness in a complex environment are greatly improved. And meanwhile, a scoring network is introduced as a post-processing module, and the grabbing feasibility of each candidate attitude is estimated in combination with target point cloud and attitude features, so that the judgment capability of the model is further improved.

The invention discloses a virtual-real synchronization system and a virtual-real synchronization method for a mathematical experiment based on cloud-edge collaboration, relates to the technical field of cloud-edge collaboration, and realizes dynamic correction of an experiment result by collecting physical experiment data at a terminal and combining a theoretical probability model. When the experimental sample size is insufficient or the experimental deviation is too large, the system can generate an adjustment factor based on the network and edge computing state, and request compensation data from the cloud. And the cloud end generates high-correlation preprocessed data by using a historical data set and combining compressed sampling processing, so that delay caused by large-scale data transmission is avoided. And then, the terminal performs weighted fusion on the physical experiment data and the compensation data according to the fusion weight to obtain a more accurate and stable mixed experiment result, and the result is visually displayed by a probability distribution curve. According to the invention, the authenticity of the experiment and the statistical reliability are considered, and the data transmission efficiency and the processing real-time performance are improved.