93 results about "Bayesian estimator" patented technology

The invention relates to the technical field of equipment full-life-cycle management, in particular to an equipment full-life-cycle tracing and management and control system based on digital threads. Comprising a digital thread generation unit which is used for generating a dynamic digital thread penetrating through the whole life cycle of technical improvement purchasing, operation maintenance and decommissioning of equipment; a full life cycle data integration unit; and a business process management and control unit. According to the method, a dynamic digital thread which runs through the whole life cycle of technical improvement purchasing, operation overhaul and decommissioning of equipment is constructed, and a multi-dimensional data anchoring algorithm fused with weighted Bayesian estimation is combined, so that probabilistic association matching between heterogeneous physical data in each stage and a corresponding physical scene is realized; and a unique device identifier is bound to form dynamic association of physical state-data record-time node, so that the problems of fragmentation of data in the whole life cycle of the device and lack of association between the data and a physical scene in the prior art are solved.

The invention discloses a degradation track-based cylinder residual life dynamic prediction method and system. The method comprises the following steps of: preprocessing multi-source degradation original data to obtain a standardized training sample set; quantizing a cylinder performance degradation rate trend by adopting a drift coefficient, quantizing cylinder performance degradation random fluctuation intensity by adopting a diffusion coefficient, and establishing a Wiener process degradation model through the drift coefficient and the diffusion coefficient; substituting the standardized training sample set into the Wiener process degradation model, carrying out parameter self-learning by fusing Bayesian estimation and an EM algorithm, and outputting a final Wiener degradation model; inputting the real-time recorded data into a preset Wiener degradation model to obtain a prediction result of the residual life of the cylinder; dynamically updating the Wiener degradation model according to the prediction result of the residual life of the air cylinder and the real-time recorded data of the air cylinder at the next time step; the online dynamic accurate prediction of the residual life of the cylinder is realized, and the prediction precision and the model environment adaptability are effectively improved.

The invention belongs to the field of power system operation and control, and particularly relates to an operation control method for a wind-light hydrogen storage multi-energy complementary system, which comprises the following steps: performing empirical mode decomposition on an original output sequence to obtain an IMF component, training parameters by combining a hidden Markov chain model and a forward-backward algorithm and solving an optimal state sequence, and fusing a predicted value by using Bayesian estimation to obtain an optimal state sequence; outputting a wind and light output and load demand global prediction value; by taking the minimum comprehensive operation cost, the minimum carbon emission intensity and the minimum power supply shortage rate as targets, obtaining equipment, power balance and hydrogen storage capacity constraints through a constraint verification algorithm, and constructing a multi-target optimization model; a scene trigger function identifies four types of typical scenes, a chaos particle swarm optimization algorithm is improved to solve a model, and density peak clustering is carried out to obtain a global scheduling scheme containing output of an electrolytic cell and a fuel cell and energy storage charge and discharge power. According to the invention, through scene adaptive optimization and intelligent clustering screening, the randomness and volatility of wind and light output are dealt with.

The invention relates to the technical field of coal mine safety monitoring, and discloses a coal mine tunnel laser radar three-dimensional dynamic monitoring method. Aiming at the problems that in the prior art, data of a laser radar is easy to interrupt or unreliable under a severe working condition, and monitoring continuity is poor due to insufficient multi-source data fusion, the method is characterized in that laser radar surface data and optical strain gauge point data are synchronously acquired; constructing a roadway digital twinborn model; carrying out fusion and assimilation on the multi-source data by utilizing a Transform mechanism to obtain initial roadway deformation data; judging the credibility of the laser radar data based on Bayesian estimation; when the data are missing or uncredible, deducing full-field deformation by utilizing a gating mechanism in combination with optical strain gauge point data and a digital twin model; and finally, generating and outputting a three-dimensional dynamic deformation field. The system is mainly used for real-time, continuous and high-reliability deformation monitoring and safety early warning of the coal mine tunnel.

The invention discloses a privacy perception graph prompt learning method based on adaptive topology recovery, which belongs to the technical field of computer network security and artificial intelligence crossing, and adopts an adaptive structure Bayesian estimation method to reconstruct privacy graph data into graph data closer to an original graph sample, and extracts an internal privacy mode from node embedding, so as to improve the privacy perception graph prompt learning efficiency. A learnable prompt of a noise mode introduced based on dynamic perception and responding to privacy disturbance is constructed, progressive privacy consistency is introduced in the optimization process, semantic alignment between multiple noise views is promoted, two loss functions are jointly optimized, multiple rounds of iterative training are performed, and the optimization efficiency is improved. According to the method, the final generalization ability of the graph prompt learning model and the balance of task adaptation can be realized, and a load balance topology is generated based on an adaptive disturbance estimation topology recovery technology so as to establish disturbance distribution; a noise suppressed reliable cue and multi-view cue optimization is designed to mitigate the effects of residual perturbations, generating robust and semantically aligned cues.

The invention provides a pipettor automatic calibration positioning and accuracy evaluation method based on photoelectric sensing, and relates to the technical field of pipettor calibration, and the method comprises the following steps: arranging an annular photoelectric sensor array to collect interference fringe phase difference, liquid level reflection light signals and surface acoustic wave signals, and adopting a five-step phase shift method to obtain a three-dimensional displacement vector; dynamic calibration is achieved through liquid level morphological parameter analysis in combination with adaptive Kalman filtering, numerical simulation is conducted on the liquid drop forming process through time-frequency analysis and a fluid mechanics model, and finally the accuracy is evaluated through adaptive fuzzy reasoning and Bayesian estimation. According to the invention, high-accuracy automatic calibration and evaluation of the pipettor are realized.

The invention discloses a point cloud intersection-based space target optical segmental arc rapid association method. The method comprises the following steps: 1, acquiring optical segmental arc data and constructing a constraint admissible domain; 2, generating a track point cloud; 3, forecasting parallel orbit point cloud; 4, fast point cloud intersection is carried out; and 5, determining an initial orbit based on point cloud intersection. Through the above process, the space target rapid segmental arc correlation method based on point cloud intersection is provided, an admissible domain is constructed by two optical observation segmental arcs and orbit point clouds are generated, and then the point clouds of the two segmental arcs are forecasted to the same moment through a parallel orbit point cloud forecasting technology. Quickly locking an overlapping region of the two point clouds by adopting a minimum bounding box method, and judging the relevance between the point clouds through a mahalanobis distance; and finally, based on the Bayesian estimation principle, carrying out initial orbit determination on the successfully associated arc segments.

The invention provides a low-altitude take-off and landing facility demand scale space distribution prediction method for a target area, and relates to the technical field of infrastructure planning, and the method comprises the steps: S1, analyzing and predicting the historical demand data of four scene types according to the historical demand data of the target area; s2, performing parameter calibration by adopting Monte Carlo simulation, and improving the prediction precision of the corrected exponential growth model and the logistic growth model through Bayesian estimation; s3, determining the demand quantity of low-altitude passenger transport take-off and landing facilities and the demand quantity of low-altitude loading take-off and landing facilities, and optimizing to obtain the total demand quantity of low-altitude take-off and landing stations in the target And S4, planning the low-altitude infrastructure of the target area, and enabling the nodes of the low-altitude take-off and landing facility to be matched with the space of the demand hotspot. According to the method, a plurality of dynamic technology progress models and a multi-scenario analysis mechanism are integrated, so that the accuracy and scientificity of data prediction are improved; and a station reuse coefficient is introduced, efficient sharing of passenger transport and freight transport facilities is realized, and the planning layout of low-altitude take-off and landing facilities is optimized.

A system and method for localizing an autonomous vehicle using mapping and real-time data. And scanning and matching the mapping data and the real-time data. Characteristics of the autonomous vehicle, such as, but not limited to, linear and angular velocities, heading, and motion prediction, are provided to a Bayesian estimation algorithm, and a final pose is calculated.

The invention provides a wide-field magnetic field imaging method based on sparse sampling and Bayesian estimation, and belongs to the field of magnetic field imaging. The method comprises the following steps: constructing an NV color center system, measuring and recording information of a magnetic field to be measured; selecting a small number of discrete sampling points to measure magnetic field intensity data in an imaging field of view of a measured area; the method comprises the following steps of: constructing an error correlation function among pixel points in a measured area, establishing a Bayesian estimation model, and predicting the magnetic field intensity of unmeasured pixel points in a full field of view by taking magnetic field intensity data of a small number of discrete sampling points as observed quantity to obtain a preliminarily reconstructed magnetic field distribution image; selecting a plurality of reference points in the measured area, acquiring nominal magnetic field values and actually measured magnetic field values of the reference points, and calibrating the preliminarily reconstructed magnetic field distribution image by adopting a mean value proportion correction mode; on the premise that the number of sampling points is greatly reduced, magnetic field distribution reconstruction with high spatial resolution and high structural similarity is achieved, and therefore imaging efficiency and reliability are improved.

This invention provides a method and related equipment for inverting the pore structure of carbonate rocks, relating to the field of geological exploration technology. The method includes acquiring test data, and using a trained deep learning prediction model with a Bayesian estimation algorithm to invert the pore structure based on the test data, thereby obtaining pore structure prediction results. The deep learning prediction model includes a Bayesian convolutional neural network layer. According to the above technical solution, test data is input into the trained deep learning prediction model, and the Bayesian estimation algorithm is used to invert the pore structure, obtaining pore structure prediction results, including confidence intervals for pore structure parameters. This not only quantifies the uncertainty of the prediction results, thereby expressing the uncertainty and obtaining confidence intervals, but also effectively improves prediction accuracy, i.e., improves the accuracy of the pore structure prediction results. It has great application potential in the field of data-driven pore structure prediction, facilitating better decision support.

The invention discloses a noise enhancement nonlinear system joint detection and estimation method under a Bayesian framework, and belongs to the field of signal processing. Firstly, independent additive noise is added to a nonlinear system input signal, and noise-corrected nonlinear system output is obtained after the signal passes through a nonlinear system. And secondly, under the Bayesian criterion, judging which hypothesis in the binary hypotheses is established by utilizing the output of a nonlinear system of noise correction, and estimating unknown parameters in the signal of which the judgment result is H1. On the premise that the detection performance is not reduced, a noise enhancement nonlinear system joint detection and estimation model which minimizes the estimation risk is constructed. The additive noise is the optimal solution of the model and is random distribution formed by not more than two constant vectors. According to the method, noise enhancement and nonlinear system joint detection and estimation under the Bayesian framework are combined, and the Bayesian estimation risk is further reduced under the condition that the Bayesian detection cost is not increased.

The invention relates to unmanned aerial vehicle path planning, in particular to an unmanned aerial vehicle path planning method and system for transmission and distribution line inspection, and the method comprises the steps: carrying out the modeling of a tower and a surrounding environment of a transmission and distribution line, and determining obstacles and detection points in the transmission and distribution line; planning a first unmanned aerial vehicle inspection path according to obstacles and detection points in the transmission and distribution line; acquiring three-dimensional point cloud data of the transmission and distribution line, and establishing a three-dimensional model; filtering the three-dimensional point cloud data by adopting a point cloud filtering algorithm to obtain obstacles in the three-dimensional model; updating the confidence coefficient of the obstacle in the three-dimensional model based on Bayesian estimation to obtain an obstacle map; calculating Euclidean distances among obstacles in the obstacle map by adopting a fast marching algorithm, and planning a second unmanned aerial vehicle inspection path according to the obstacle map and the Euclidean distances; the technical scheme provided by the invention can effectively overcome the defects that the method is difficult to adapt to dynamically changing obstacle distribution, obstacles in a complex environment cannot be accurately recognized, and potential safety hazards of path planning are large.

The present application relates to a kind of statistical inference method of aero-engine combustion chamber net damping coefficient, statistical inference system, statistical inference equipment and computer readable storage medium.The statistical inference method includes S1, obtains the dynamic pressure sampling data of combustion chamber;S2, calculates pressure autocorrelation function value;S3, based on pressure autocorrelation function value, deduce the analytic function relationship satisfied between pressure autocorrelation function value, eigenfrequency and damping coefficient;S4, according to the principle of Bayesian estimation, based on autocorrelation function value, infers the joint probability density function of eigenfrequency and damping coefficient;S5, sample value is obtained from joint probability density function, and eigenfrequency and damping coefficient are estimated based on sample value.The present application proposes a kind of statistical inference method of aero-engine combustion chamber net damping coefficient, statistical inference system, statistical inference equipment and computer readable storage medium, and the damping coefficient of aero-engine combustion chamber can be effectively estimated.

The invention discloses an auditable asset allocation system and method based on time-varying risk preference estimation. The system comprises a data acquisition and preprocessing module; the time-varying risk preference estimation module is used for carrying out online Bayesian estimation on the risk preference time-varying hidden state by adopting a sequential Monte Carlo method based on the state space model; the dynamic asset allocation optimization module is used for taking the estimated value as an endogenous variable to be embedded into an optimization model to solve the optimal weight; and the compliance and auditing module is used for recording the full-link log in a tamper-proof manner and generating an interpretable report. According to the method, closed-loop optimization from dynamic risk preference estimation to asset allocation decision is realized, and auditing performance of the whole process is ensured.

This invention relates to the field of earthquake disaster emergency monitoring technology, specifically to an intelligent system and method for identifying earthquake surface rupture zones. The system includes modules for heterogeneous data registration, feature extraction and fusion, rupture zone segmentation, active learning, and morphological optimization. It achieves accurate processing of remote sensing data and intelligent identification of rupture zones. The system employs multi-scale feature point matching and adaptive weight registration, improving data registration accuracy by over 30% and overcoming the reliance on human experience in traditional methods. Through multi-modal feature decoupling and attention-guided feature fusion, combined with multi-scale spatial pyramid pooling and deformation-sensitive segmentation, the system effectively identifies rupture zones and assesses prediction uncertainty. The active learning module optimizes training data through Bayesian estimation and a hybrid sampling strategy, while the morphological optimization module utilizes geological constraints and fault topology inference to ultimately output accurate rupture zone distribution and attribute information, improving the intelligence level and efficiency of earthquake disaster emergency monitoring.

The invention relates to a crane intelligent examination method and system based on thermal imaging and a medium in the technical field of crane training. The crane intelligent examination method comprises the steps that infrared heat source data and coordinates of crane structure nodes and skeleton node coordinates of an operator are obtained and processed to obtain a high-dimensional feature vector Z, and a preliminary score is obtained after linear mapping; and processing the X through a fuzzy logic rule and a Bayesian estimation method, and inputting the X and the X into the evaluation neural network to obtain a secondary score. And fusing the preliminary score and the secondary score to obtain a final score, and if the final score is greater than a threshold value, determining that the examination passes. According to the invention, by combining the infrared thermal imaging technology and the skeleton identification technology, dominant behaviors of examinees in operation and hidden influences of the dominant behaviors on equipment can be captured at the same time, and the potential bad operation habits are reflected in final scores, so that the real-time state and the actual capability of an operator can be comprehensively reflected.

The application provides a composite sandwich structure material damage identification method based on a Bayesian fusion algorithm, and the method comprises the following steps: extracting the time of flight of a scattered wave signal based on continuous wavelet transform; wherein the time of flight refers to the time for which the scattered wave signal generated by an excitation sensor is transmitted to a receiving sensor; obtaining a likelihood function based on the time of flight and an elliptical positioning method; determining a correlation coefficient based on a damage signal and a reference signal; in a RAPID method, taking the correlation coefficient as a damage sensitivity characteristic to determine a prior probability density function of a damage position; performing Bayesian estimation based on the likelihood function and the prior probability density function to obtain a posterior probability density function of the damage position; and fitting a damage positioning image using a Markov Monte Carlo method (MCMC) based on the posterior probability density function of the damage position.

The invention provides a high-speed rail 5G signal distribution circuit diagram generation method based on Bayesian estimation positioning, and the method comprises the steps: obtaining target network data, BDS positioning information and LBS ranging information, generating the positioning information of a current circuit library, and obtaining the positioning information of a historical circuit library; predicting first predicted position information at the next moment according to the current line library positioning information, and predicting second predicted position information according to the historical line library positioning information; target prediction position information at the next moment is determined through Bayesian estimation, and the actual position information is corrected into target position information; and generating a high-speed rail 5G signal distribution circuit diagram based on the target network data and the corrected target position information. According to the technical scheme provided by the embodiment of the invention, the historical experience position, the BDS positioning information and the LBS ranging information can be utilized, the next position can be predicted according to the Bayesian estimation to carry out position correction, and the high-speed rail 5G signal distribution circuit diagram can be accurately generated under the condition of reducing the hardware cost.

A signal processing apparatus includes: a data input unit to which image data is input; an output unit configured to output an output value based on the data input to the data input unit; an expectation feedback calculator configured to calculate a difference between an expectation based on the input data and the output value; and a Bayesian estimator to which information on the difference and information based on the image data are input and which is configured to perform machine learning in order to approximate the output value to the expectation based on the input information and to search for an optimum configuration.

Techniques for aerial vehicle tracking using dynamic aleatoric uncertainty covariance estimation are presented. The techniques include: obtaining an image depicting at least one aerial vehicle of interest; passing the image to a first machine learning subsystem, which provides at least one feature vector; inputting the at least one feature vector to a second machine learning subsystem, where the second machine learning subsystem is trained to provide detected aerial vehicle identification data sets (including respective aerial vehicle coordinates, respective aerial vehicle bounding box dimensions, and respective dynamic aleatoric uncertainty covariance values) corresponding to input feature vectors; providing at least one detected aerial vehicle identification data set to a recursive Bayesian estimator subsystem, from which at least one filtered set of aerial vehicle coordinates, representing a real-time location of a respective aerial vehicle of interest, is obtained; and outputting the at least one filtered set of aerial vehicle coordinates.

This invention relates to a Bayesian estimation-based method for suppressing cross-polarization SAR noise, comprising: acquiring raw cross-polarization data and preprocessing it; acquiring noise vector information corresponding to the cross-polarization data and stripe boundary information of each sub-band; calculating the noise scaling factor of the sub-band based on the Bayesian method; calculating the power balance factor of the sub-band; and subtracting the reconstructed two-dimensional noise field from the backscattering coefficient of the raw cross-polarization data to achieve denoising. The beneficial effects of this invention are: it reads and calculates noise vector information from data products, obtains noise scaling factors through Bayesian estimation, and scales the noise vector to achieve the purpose of suppressing azimuth noise.

The present application relates to a kind of wind speed prediction method based on coal mine underground gas data fluctuation similarity, belong to coal mine underground safety monitoring technical field, the method includes: at least 3 adjacent methane sensors of deployment in target roadway, collect gas concentration time series data and carry out pre-processing;Identify gas abnormal fluctuation event, extract the local and time sequence characteristics of fluctuation waveform by CNN-LSTM network, output high-dimensional feature vector;Fluctuation signal of the same source is matched using improved Siamese neural network, and the position time difference of characteristic waveform is calculated by DTW algorithm;Calculate single group wind speed in combination with sensor spacing, and obtain preliminary wind speed by weighted average;Through bayesian estimation, the optimization result of the gas concentration of multiple devices is fused, and then error feedback is realized to realize online optimization of model.The present application does not need to rely on special wind speed equipment, and has high prediction accuracy and strong robustness, can provide reliable data support for underground gas management and ventilation control.

The invention relates to a multi-sensor intelligent refrigerator detection terminal and a data fusion method, and belongs to the technical field of intelligent manufacturing. The multi-sensor intelligent refrigerator detection terminal comprises a camera, a temperature and humidity sensor, a hydrogen sulfide gas sensor, a photosensitive sensor and a core control chip; the core control chip collects image data, temperature and humidity data and hydrogen sulfide gas concentration data measured by the camera, the temperature and humidity sensor and the hydrogen sulfide gas sensor; data output by the hydrogen sulfide gas sensor and the image sensor are collected, abnormal values in the data are removed according to a data abnormal value removing method of Euclidean distance, the processed data are fused by adopting a fusion algorithm, and whether the food freshness is met or not is judged according to the fused data. Abnormal data are removed through a data abnormal value removal method of Euclidean distance to obtain effective data, and then the freshness of food is judged through a multi-sensor data fusion method based on Bayesian estimation, so that the accuracy of food freshness collection by sensors is improved.

The invention discloses aerial vehicle tracking using dynamic accidental uncertainty. Techniques are provided for aerial vehicle tracking using dynamic accidental uncertainty covariance estimation. The techniques include obtaining an image depicting at least one aerial vehicle of interest; passing the image to a first machine learning subsystem, which provides at least one feature vector; inputting the at least one feature vector into a second machine learning subsystem, wherein the second machine learning subsystem is trained to provide a detected aerial vehicle identification data set (including respective aerial vehicle coordinates, respective aerial vehicle bounding box sizes, and respective dynamic accidental uncertainty covariance values) corresponding to the input feature vectors; providing the at least one detected set of aerial vehicle identification data to a recursive Bayesian estimator subsystem from which at least one set of filtered aerial vehicle coordinates representing a real-time location of a corresponding aerial vehicle of interest is obtained; and outputting at least one set of filtered aerial vehicle coordinates.

The application discloses a kind of automobile wheel positioning parameter bayesian estimation system and method, to solve automobile wheel positioning parameter bayesian estimation and need pre-calibration before detection problem.Automobile wheel positioning parameter bayesian estimation system mainly by camera (1), wheel target (2), target connection clamp (3), clamp (4) and tripod (5).Tribrach (5) is placed on horizontal ground, target connection clamp (3) is the section U-shaped cuboid of steel plate processing and two solid and 120-140 degree obtuse angle cylinder welding parts, automobile wheel positioning parameter bayesian estimation method is by automobile wheel positioning parameter bayesian estimation data acquisition, automobile wheel positioning parameter bayesian estimation training and test, automobile wheel positioning parameter bayesian estimation weight vector optimization etc.composition of step, provide a kind of structure simple, detection precision is high, need not pre-calibrated automobile wheel positioning parameter bayesian estimation system and method.

The invention provides a non-reference damage imaging method based on nonlinear Lamb waves and Bayesian inference, and relates to the field of damage imaging, and the method specifically comprises the following steps: collecting nonlinear Lamb wave signals of a plurality of propagation paths; an enhanced second harmonic signal is obtained through a pulse inversion technology; extracting a flight time feature of the envelope signal; the frequency spectrum of the nonlinear Lamb wave signal is obtained through fast Fourier transform, and nonlinear coefficient damage index features are obtained through calculation; feature level fusion is carried out based on Bayesian estimation; and solving a posterior probability density function of the damage position parameter by using Hamiltonian Monte Carlo sampling, and carrying out damage probability imaging positioning according to posterior probability distribution. According to the technical scheme, the problems that in the prior art, a large number of sensors usually need to be deployed, and the dual requirements for high-precision damage detection and sensor number minimization in engineering practice are difficult to meet are solved.

The application discloses a hydrogen energy storage system electrolytic cell selection method for complex multiple working conditions, comprising the following steps: according to existing large number platform data statistics results, determining different renewable energy hydrogen production scene probability distributions of various regions, multiple type electrolytic cell use probabilities and multiple type electrolytic cell use probabilities under different scenes, taking the data as input (prior probability matrix) of an electrolytic cell selection decision system, and then calculating electrolytic cell use probability matrix (posterior probability matrix) under different application service working conditions through a Bayesian estimation method according to the known prior probability. According to the dynamic correction of the Bayesian formula, the use probability of different types of electrolytic cells under different hydrogen production scenes is corrected, the multi-subject matching characteristics of the renewable energy hydrogen production and energy storage system are effectively ensured, the distribution problem of maximum resource utilization and maximum economic benefit in the renewable energy hydrogen production system is solved, and the economic construction of the hydrogen energy storage system applied to multiple types of scenes in the future can be effectively guaranteed.

Systems, methods, and computer program products described herein for building a prediction model with varying availability. Embodiments described herein build a prediction model that is initially trained with a selected subset of data characteristics (e.g., data characteristics that are commonly available in training data), and then augmented via Bayesian estimation with additional data characteristics when such data characteristics are available.

The invention relates to the field of power distribution network section positioning, and discloses a DG-containing power distribution network fault area positioning method and system for multi-source information fusion and verification, and the method comprises the steps: obtaining the output current of a new energy power supply through a U-I mapping curve family which is practical in engineering, and carrying out the dynamic adjustment of an FTU alarm threshold value according to the different output conditions of the new energy power supply; dividing the credibility levels of FTU, TTU and court ammeters, identifying the true degree of each piece of measurement information, and reducing the dimensionality of the solution space by using the high-credibility FTU, TTU and court ammeters; and solving the basic distribution probability of the fault hypothesis variable by using the distortion information sequence and the Bayesian estimation model, and positioning the fault section by checking the positioning result through the consistency of the section positioning result and the port positioning result. And through a multi-source information fusion and verification technology and in combination with alarm information of FTU, TTU and court ammeters, the accuracy and efficiency of positioning the fault section of the power distribution network are effectively improved. The fault area can be accurately judged, and false alarm and missing alarm are reduced.