45 results about "Radial basis function network" patented technology

The invention provides a coordination control method for a photovoltaic off-grid hydrogen production system, which comprises the following steps of: acquiring voltage and current time domain signals of a photovoltaic array, constructing an impedance frequency characteristic mapping model through tensor decomposition and a radial basis function network, and calculating converter control parameters; based on converter control parameters and environment data, temperature-sensing-free self-correction photovoltaic characteristic prediction is achieved through electrical parameter indirect temperature estimation, and an MPPT reference track and a power prediction track are generated; multi-time-scale data coordination processing of millisecond-level electrical control, second-level pressure regulation and minute-level temperature optimization is executed, potential conflicts are solved, and an execution instruction set is generated; and generating and issuing a system comprehensive control instruction by adopting a double-layer deep reinforcement learning network. According to the method, the system efficiency and stability are improved, and the performance is excellent especially under the illumination fluctuation working condition.

The invention belongs to the technical field of optical detection, and particularly relates to a global power wave aberration prediction-based Ritchey-Common detection method and system, and the method comprises the steps: firstly building a three-dimensional optical model through the parameters of a standard spherical mirror, a to-be-detected plane mirror and an interferometer, and combining a preset Ritchey angle and an out-of-focus / astigmatism coefficient, injecting vibration and temperature noise by using Monte Carlo simulation to generate an enhanced training data space; secondly, constructing a phase error adjustment model based on an adversarial network, and decoupling a mapping relation between an adjustment error and wave aberration distortion through a multi-scale attention mechanism and a radial basis function network to realize adaptive correction of sub-aperture phase data; and finally, splicing sub-aperture phases by using a graph neural network and fusing multi-angle full-aperture measurement data to accurately reconstruct the surface shape of the plane mirror to be measured. According to the invention, through confrontation training and dynamic error compensation of physical constraints, the detection efficiency and reliability of the large-aperture optical element are significantly improved.

The invention relates to the technical field of medical image analysis, in particular to a left ventricular compliance assessment method and system based on medical image.The method comprises the following steps that a planar three-dimensional coordinate from a left ventricular apex to a mitral annulus is obtained through a CT image, a thin-plate spline interpolation algorithm is called to establish a left ventricular long-axis model, the ultrasonic end-stage internal diameter is obtained synchronously, and a left ventricular compliance assessment result is obtained. And inputting the left greenhouse long axis model and the ultrasonic diastole end-stage inner diameter into an Euclidean distance calculation function to generate a left greenhouse length-diameter ratio. According to the method, a left greenhouse long axis model is constructed by integrating CT three-dimensional coordinates and ultrasonic inner diameter, the length-diameter ratio is calculated by combining Euclidean distance, point cloud registration is carried out, a myocardial wall area is segmented, local deformation is quantized by utilizing Gaussian curvature gradient, a correction factor is generated by inputting a radial basis function network, and a parameter coupling mechanism is established by fusing an aorta root angle. And optimizing the weight of the pressure-volume equation, establishing a dynamic model, and realizing a quantitative evaluation system of spatial heterogeneity and time continuity.

The invention discloses a mechanical arm iterative learning control method based on a radial basis function network. The method solves the limitation that a traditional mechanical arm control method deals with nonlinear characteristics and external disturbance problems in a complex dynamic environment. The method provided by the invention comprises the following steps: firstly, constructing a mechanical arm system dynamic model, and dynamically correcting and optimizing a reference track by adopting a dynamic correction strategy; then designing a radial basis function neural network to construct a nonlinear compensation item, and designing a dynamic weight parameter and a dynamic learning gain for optimizing the performance of the controller; and finally, designing an iterative learning controller, and verifying the stability and error convergence of a control algorithm. Through the radial basis function network, the dynamic adjustment strategy and the iterative learning strategy, the control precision, the adaptive capacity and the error convergence speed of the mechanical arm system are improved.

The invention discloses a time-varying state constraint terminal sliding mode tracking control method based on an adaptive network, and relates to the technical field of adaptive control, and the method comprises the steps: 1, constructing a constraint potential energy error for each actual state of an engineering controlled object; 2, constructing a composite sliding mode variable with a fixed rational index; all sliding mode variables of all actual states of the project controlled object form an integral sliding mode surface on a vector level; and 3, carrying out online approximation on the actual dynamic state of the engineering controlled object by utilizing the single-hidden-layer radial basis network, and introducing network output as a dynamic compensation item into control input, thereby ensuring that the whole sliding mode surface is driven to zero within finite time and synchronously completing real-time compensation on the actual state of the engineering controlled object. According to the invention, the finite time high-precision tracking control of the controlled object of the controlled project with unknown nonlinearity and double-side time-varying boundaries is realized.

The invention discloses a multi-fan operation system and a self-adaptive control method and device. The method comprises the steps that operation state data of fans are collected in real time through a sensor assembly; if the data fluctuation amplitude in the operation state data is greater than a first preset threshold value, performing Kalman filtering processing on the operation state data to obtain first prediction data; if the data fluctuation amplitude in the first prediction data is greater than a second preset threshold value, inputting the first prediction data into the radial basis function network model to obtain second prediction data; and the second prediction data are sent to a PID controller, PID controller parameters are adjusted through a particle swarm algorithm, and the target rotating speed in the fan running process is obtained. Through integration of the Kalman filtering technology radial basis function network model and the PID controller, accurate state estimation, dynamic modeling prediction and real-time adaptive control of the multi-fan system can be realized, complex working conditions such as wind speed and load fluctuation can be effectively handled, and the overall performance of the system is improved.

The invention relates to the technical field of industrial automation control, and discloses a mixed PID temperature control system and method based on GDNN and multi-modal prediction. The system comprises a data acquisition module, an enhanced GDNN core controller, a prediction optimization module and an actuator module. And real-time adaptive tuning of PID parameters (Kp, Ki and Kd) is realized by fusing a general dynamic neural network, a genetic algorithm optimization initial weight, a variable learning rate and an additional momentum method, and a prediction model module of a multi-layer perceptron (MLP) and a radial basis function (RBF) network. According to the system, nonlinear, time-varying and interference factors can be processed for temperature control scenes such as a high-temperature furnace and a drying system, the mean absolute error, overshoot and stabilization time are remarkably reduced, and the energy efficiency is improved. A multi-modal sensor fusion and edge computing framework is innovatively introduced, and distributed industrial application is supported. The system is suitable for the fields of high-temperature furnaces, drying systems, intelligent buildings and the like, and provides high robustness and interpretability.

The embodiment of the invention discloses an air compressor control system, a control method and an air compressor. The control system comprises a data acquisition module, a control module and a power module, the data acquisition module is used for acquiring air pressure parameters of a target air compressor at set time intervals; the control module optimizes PID control parameters by using a radial basis function network and a particle swarm optimization algorithm on the basis of the air pressure parameters, adjusts the optimized PID control parameters in real time by using a fuzzy control method, determines PID output quantity by using the adjusted PID control parameters, and controls the power module to act on the basis of the PID output quantity; and the power module is used for driving the target air compressor to act under the control of the control module. The technical problems that in the prior art, due to the fact that an air compressor adopts traditional PID control, the control precision is insufficient, the dynamic response is poor, and the anti-interference capacity is poor are solved.

The present invention provides a method for identifying potential boundaries of urban development for national land planning, comprising the following steps: step 1, determining the geographic coordinates of the core points of the city based on multi-year nighttime light remote sensing data; step 2, digitizing the historical map based on remote sensing images and geographic information system technology, and identifying the historical city boundaries; step 3, calculating the distance between the core points of the city and the historical city boundaries; step 4, extracting the extended distance between the radiation lines and the historical city boundaries over many years, and generating a data set; step 5, constructing a radial basis function network; step 6, training the data set based on the radial basis function network, and obtaining the predicted boundary expansion distance by inputting the relevant attributes of the predicted expansion. The present invention can simplify the computational complexity of identifying boundaries, improve the efficiency of identifying urban boundaries, and more accurately detect and simulate urban development boundaries with less data.

The invention belongs to the field of converter transformer temperature monitoring, and relates to a converter transformer winding temperature monitoring method based on digital twinning, and the method comprises the steps: building a converter transformer electromagnetic thermal coupling two-dimensional simulation model, and inputting actual operation current data as an excitation source into the two-dimensional simulation model for simulation calculation; performing electromagnetic simulation according to ampere-turn balance to obtain a winding temperature rise distribution cloud picture under different working conditions; inputting the data under the operation condition into a machine learning model for training to obtain a rapid calculation model based on a radial basis function network; a real-time current value of each winding during operation is collected by a sensor, the real-time current values are imported into a rapid calculation model, second-level real-time display of winding temperature distribution is realized, and the load is fed back and adjusted according to a set alarm threshold value. According to the method, a multi-working-condition electromagnetic thermal simulation model is established, temperature field simulation data are extracted and input into a machine learning model for data analysis, and the converter transformer winding temperature field digital twinborn body is achieved.

Embodiments of the invention provide a control method and apparatus, and an electronic device. The method comprises the steps of determining current information of a Stewart platform; the current information comprises a current pose and the rod length of each rod; inputting the current information into a prediction model to obtain adjustment information of the Stewart platform; wherein the prediction model is a network model obtained by optimizing a radial basis function network through a particle swarm optimization algorithm; control information of the Stewart platform is determined according to the adjustment information; and adjusting the current information according to the control information.

The invention discloses an SAR (Synthetic Aperture Radar) image geometric correction method and system for optimizing a radial basis function neural network based on a genetic algorithm. The method comprises the following steps: normalizing three-dimensional geographic coordinates of ground feature points, and inputting the normalized three-dimensional geographic coordinates to a full connection layer for feature extraction; constructing a ReRBF network, wherein the ReRBF network is formed by stacking a plurality of layers of RBF sub-modules through a residual connection structure; and the output of the previous module is used as a residual error, is spliced with the original input or the intermediate feature, and is jointly used as the input of the next module to form a progressive feature enhancement and residual error learning mechanism, so that the network can refine the coordinate mapping relationship layer by layer. And a DBSCAN algorithm is adopted to perform automatic clustering on training data, so that the generalization ability of the model in multiple scenes is improved. And optimizing the ReRBF network by using a genetic algorithm, and adaptively determining the optimal width parameter of the radial basis function network. According to the method, the geometric positioning precision and the processing efficiency of the SAR image under the complex terrain condition are improved.

The application discloses a mechanical arm iterative learning control method based on a radial basis function network, which solves the limitations of traditional mechanical arm control methods in dealing with nonlinear characteristics and external disturbance problems in a complex dynamic environment. The method comprises the following steps: firstly, constructing a mechanical arm system dynamics model, and adopting a dynamic correction strategy to dynamically correct and optimize a reference trajectory; then, designing a radial basis function neural network to construct a nonlinear compensation term, and designing dynamic weight parameters and dynamic learning gains for optimizing the performance of a controller; finally, designing an iterative learning controller, and verifying the stability and error convergence of the control algorithm. Through the radial basis function network, the dynamic adjustment strategy and the iterative learning strategy, the control precision, adaptability and error convergence speed of the mechanical arm system are improved.

A method for controlling a torque of at least one wheel of a mobile platform. The method includes: providing at least one current slip value of the wheel and at least one current wheel acceleration of the wheel as input values; providing a trained radial basis function network designed to determine, by means of the input values, at least one torque change as an output value for control of the at least one wheel; and determining a current torque change, by means of the trained radial basis function network and the provided input values, for control of the torque.

The invention discloses an aeromagnetic interference compensation method based on a radial basis function network. The method comprises the following steps: taking a 16-term coefficient matrix as input data of a deep neural network; a hidden space is constructed for data in hidden nodes in a hidden layer by using a radial basis function, the hidden layer performs certain conversion on input data, and low-dimensional mode input data is converted into a high-dimensional space, so that a linear inseparable problem in the low-dimensional space becomes linearly separable in the high-dimensional space; training a sample set of aeromagnetic data to obtain a network center and other weight parameters meeting supervision requirements; fitting the aeromagnetic interference by using the obtained weight parameter information to obtain predicted magnetic interference; and calculating the compensated standard deviation according to the predicted magnetic interference. The anti-interference capability of the model can be effectively improved.

The present invention discloses a real-time prediction method and device for the contamination degree of oil-based drilling fluid filtrate, the method comprising: extracting formation fluid by using a sampling instrument to obtain a first spectral absorbance of the formation fluid; the formation fluid comprises drilling fluid filtrate and original formation oil and gas; determining a second spectral absorbance corresponding to the drilling fluid filtrate; inputting the first spectral absorbance and the extraction factor into a trained absorbance prediction model to predict the predicted third spectral absorbance of original formation oil and gas; the absorbance prediction model is obtained based on radial basis function network training; the contamination degree of the drilling fluid filtrate is calculated based on the first spectral absorbance, the second spectral absorbance and the predicted third spectral absorbance to determine the contamination degree that meets the extraction conditions. The predicted third spectral absorbance is obtained based on the extraction factor through the absorbance prediction model, and then the contamination degree of the drilling fluid filtrate is accurately calculated to determine the timing of extracting formation oil and gas that meets the requirements.

The present invention discloses a rapid measurement system for a rectangular workpiece under three-coordinate structured light, which relates to the field of rapid measurement technology. The system comprises: an acquisition module, which is used to project four-directional orthogonal structured light through a dual projector, and cooperate with a high-speed industrial camera and a six-degree-of-freedom robotic arm to acquire a coarse positioning point cloud of the rectangular workpiece; an encoding module, which is used to generate a combined encoding sequence of Gray code and phase-shift stripes, obtain the wrapped phase through phase solution, calculate the heterodyne absolute phase using dual-frequency stripes, perform phase expansion in combination with a confidence weight map, and compensate for projection distortion through a radial basis function network; a point cloud processing module, which marks the point cloud as a plane, edge or corner area through semantic segmentation, imposes a normal vector verticality constraint on the plane area, imposes a straightness constraint on the edge area, and imposes a curvature constraint on the corner area, and determines whether the measurement is qualified by calculating a comprehensive error, thereby ensuring the accuracy and reliability of the measurement result.

The application discloses a kind of intelligent sports movement state analysis method, system, equipment and medium based on multi-module collaborative optimization.Method includes: using lightweight real-time model to video stream is human detection and initial key point positioning;Through radial basis function network, initial key point is nonlinear fine-tuning to improve accuracy;From the key point sequence after fine-tuning, extract high-dimensional kinematic feature, and utilize principal component analysis to reduce dimension, obtain core low-dimensional time sequence feature;Finally, through time sequence neural network, feature sequence is modeled, realizes action recognition and posture evaluation.The application passes through the collaborative optimization link of "perception-fine-purification-understanding", effectively solves the problems, such as imbalance of accuracy and speed, feature redundancy, time sequence modeling deficiency and error accumulation, in prior art, provides efficient, accurate, automated analysis solution for wisdom sports training.

The invention discloses an oil-based drilling fluid filtrate pollution degree real-time prediction method and device, and the method comprises the steps: extracting a formation fluid through a sampling instrument, and obtaining the first spectral absorbance of the formation fluid; the formation fluid comprises drilling fluid filtrate and undisturbed formation oil gas; determining second spectral absorbance corresponding to the drilling fluid filtrate; inputting the first spectral absorbance and the extraction factor into an absorbance prediction model obtained by training, and predicting to obtain a predicted third spectral absorbance of the oil and gas in the undisturbed stratum; the absorbance prediction model is obtained based on radial basis function network training; and calculating the pollution degree of the drilling fluid filtrate according to the first spectral absorbance, the second spectral absorbance and the predicted third spectral absorbance so as to determine the pollution degree meeting the extraction condition. The absorbance of the predicted third spectrum is obtained based on the extraction factors through an absorbance prediction model, then the pollution degree of the drilling fluid filtrate is obtained through accurate calculation, and the opportunity for extracting stratum oil gas meeting the requirements is determined.

The application discloses a kind of cable fault prediction processing method, device and electronic equipment.Therein, the method comprises: obtaining the partial discharge characteristic data of high-voltage cable;First training set data in partial discharge characteristic data is respectively input to initial radial basis function network model and initial long short-term memory network model for training;First test set data in partial discharge characteristic data is respectively input to the radial basis function network model after training and the long short-term memory network model after training, obtain the first output result output by the radial basis function network model after training, and the second output result output by the long short-term memory network model after training;Based on first output result and second output result, determine cable fault prediction model.The present application solves the technical problems of low cable fault prediction efficiency and poor prediction accuracy caused by poor performance of cable fault prediction model in related art.

The embodiment of the application provides a three-dimensional induction welding coil optimization design method and related equipment, and belongs to the field of advanced manufacturing and electromagnetic heating technology. The method comprises the following steps: discretizing a three-dimensional design space, defining geometric and topological constraints, and constructing a scalarization objective function; according to the obstacle layout, the homotopy class is divided, the representative curve is generated as an initial topological template; a differential homeomorphism mapping parameterized by a radial basis function network is constructed to drive the representative curve to deform geometrically; a serialization decision strategy is adopted, a fast proxy model based on the Biot-Savart law and a local gradient adaptive step are combined to iteratively optimize the deformation parameters at low cost; the candidate path is verified by high-fidelity finite element simulation, and the design meeting the engineering index and having the maximum and minimum performance margin is selected as the final result. The application significantly reduces the calculation cost while strictly guaranteeing the physical feasibility of the coil, and improves the efficiency and yield of complex structure design.

The invention discloses a chip electromagnetic interference prediction system and method based on adaptive scanning, and relates to the technical field of intelligent chips, the system comprises the following components: a two-dimensional automatic scanning platform, a low-frequency magnetic field probe, a signal acquisition module and a data processing and analysis module; the two-dimensional automatic scanning platform comprises an X-axis moving mechanism, a Y-axis moving mechanism, a stepping motor and an Arduino microcontroller, the X-axis moving mechanism and the Y-axis moving mechanism are both provided with a sliding rail system, the stepping motor is in driving connection with the corresponding axis moving mechanism, the Arduino microcontroller is electrically connected with the stepping motor, the Arduino microcontroller outputs a control signal to drive the stepping motor to operate, and the sliding rail system is electrically connected with the Arduino microcontroller. The stepping motor drives the low-frequency magnetic field probe to move in a plane in a chip radiation field; according to the method, electromagnetic interference space distribution reconstruction and prediction are realized based on the radial basis function network (RBFN) and singular value decomposition (SVD), the defect in the aspect of data modeling stability in the prior art is overcome, and the prediction precision is improved.

A method for reconstructing images, an electronic device and a storage medium is provided. In the method, the electronic device obtains an image to be reconstructed of an object captured by a target camera device, and obtains a sample image of the object. A radial basis function network is trained based on the image to be reconstructed and the sample image. An image reconstruction model is obtained by converting the radial basis function network based on a deconvolution algorithm, the image reconstruction model is configured for reconstructing images captured by the target camera device. By performing the method, a clarity of the image to be reconstructed can be improved.

The application discloses a kind of servo motor system control parameter intelligent optimization method, this method is by constructing radial basis function optimization network, with the characteristic parameter of given position trajectory as the input of optimization network, control parameter as output, directly to servo motor system multiple control parameters are optimized;Optimization process needs to train radial basis function network, with the mean square error of given position trajectory and actual position trajectory as loss function, the undetermined parameter of radial basis function network is updated using gradient direction propagation algorithm, when network parameter converges, the output control parameter is the optimal control parameter of optimization completion.Therefore, the feature of the present application is that network structure is simple, and the amount of calculation is small, without understanding servo system control structure, can efficiently complete the simultaneous optimization of multiple parameters.

This invention relates to the field of partial discharge pattern recognition technology, specifically to a partial discharge pattern recognition method based on adaptive mathematical morphology. The method first acquires partial discharge signal data from defective equipment and plots PRPD (partial discharge pattern recognition) maps. After grayscale and binarization processing, binary discharge images are obtained, and a dataset is constructed. Then, adaptive mathematical morphology parameter optimization is performed on the binary discharge images in the training set. The optimal results are applied to extract morphological change features and multi-dimensional morphological structure features from the binary discharge images in the entire dataset. Finally, the extracted features are weighted and fused, input into a radial basis function network for pattern recognition, and the recognition result is output. This invention effectively improves the accuracy of partial discharge pattern recognition through adaptive morphological parameter optimization and multi-dimensional feature fusion, and is suitable for insulation condition monitoring and fault diagnosis of power equipment.

A Yellow River Ningxia section ice flood prediction method comprises the following steps: S0, collecting daily weather, highest temperature, lowest temperature and daily average runoff information in a monitored river reach in a specific historical period, and constructing a data set by taking the weather, the highest temperature and the lowest temperature as input and the daily average runoff as output; s1, constructing an ice flood prediction model by adopting the data set and taking a support vector regression model and a radial basis function network model as basic models; s2, on the basis of the ice flood prediction model, a corresponding change rule between the daily average runoff volume and the weather, the highest air temperature and the lowest air temperature is obtained; s3, collecting daily weather, highest temperature and lowest temperature information in a future prediction period in the monitored river reach; s4, inputting the weather and temperature information in the S3 into an ice flood prediction model, and obtaining daily average runoff information of the monitored river reach in a future prediction time period; and S5, predicting the ice flood level of the monitored river reach in the future prediction time period.

The invention relates to the field of automobile transmission, in particular to a locking control method and system of a hydraulic torque converter based on a radial basis function. Acquiring working condition data; constructing a radial basis function network decision model according to the working condition data, and outputting a blocking coefficient based on the radial basis function network decision model; and the locking coefficient is compared with a locking threshold value or an unlocking threshold value according to the vehicle state, and a locking instruction or an unlocking instruction is sent to the hydraulic torque converter. According to the method, the radial basis function network decision model is constructed by obtaining the vehicle working condition data, the nonlinear fitting capability and the local response characteristic of the radial basis function network decision model are utilized, complex working condition characteristics are converted into locking coefficients to be output, and the energy loss in the hydraulic transmission stage can be reduced to the maximum extent according to the locking coefficient generation instruction; in addition, the pause feeling in the locking process is effectively restrained through the radial basis function network decision model, and particularly under the complex road condition that loads change frequently, the driving comfort is improved.

The present invention relates to an intelligent detection method for the nitrogen content in the effluent of the urban sewage treatment process based on a cascaded modular neural network. First, the input variables of the model are selected through mutual information, and Module 1 is established to predict the effluent NH4-N. Then, considering the high correlation between the effluent NH4-N and the effluent TN, the predicted value of the effluent NH4-N in Module 1 is combined with other easily measurable variables to design Module 2 for predicting the effluent TN. In addition, to ensure the efficiency and generalization performance of the entire soft measurement model based on HMN, the two modules are constructed by a radial basis function (GM-RBF) network based on the growth and merging mechanism. Through the node growth and merging strategy and the second-order learning algorithm, the computational efficiency and generalization ability of the model are improved. Finally, the effectiveness of the soft measurement model based on HMN is evaluated through actual industrial data, and the present invention effectively solves the problem that it is difficult to accurately detect the ammonia nitrogen and total nitrogen in the effluent in real time during the urban sewage treatment process due to environmental or instrument limitations.

This invention relates to the field of materials data modeling technology, specifically to a method for constructing a thermal processing map for metallic materials. The method includes the following steps: calculating the true stress-strain sequence; fitting the true stress-strain sequence to a continuously differentiable three-dimensional rheological response surface based on a radial basis function network; performing analytical differentiation on the three-dimensional rheological response surface to extract global strain rate sensitivity field data; calculating the power dissipation efficiency matrix and rheological instability judgment parameters based on the strain rate sensitivity field data; and then generating a thermal processing map. In this invention, by establishing a continuously responding surface model based on global optimization and using analytical derivative solutions to replace the traditional differential calculation of discrete data, the numerical oscillation phenomenon caused by experimental noise is effectively eliminated, ensuring thermodynamic consistency in the constitutive relation calculation process, significantly improving the geometric accuracy of the power dissipation efficiency contour lines, and achieving accurate prediction of the safe processing window for metallic materials under complex thermal deformation conditions.

The invention discloses a rapid measurement system for a cuboid workpiece under three-coordinate structured light, and relates to the technical field of rapid measurement, and the system comprises an acquisition module which is used for projecting four-direction orthogonal structured light through double projectors, and is matched with a high-speed industrial camera and a six-degree-of-freedom mechanical arm to acquire a coarse positioning point cloud of the cuboid workpiece; the coding module is used for generating a combined coding sequence of Gray codes and phase shift fringes, obtaining a wrapped phase through phase calculation, calculating a heterodyne absolute phase by using the double-frequency fringes, performing phase unwrapping in combination with a confidence coefficient weight map, and compensating projection distortion through a radial basis function network; and the point cloud processing module marks the point cloud as a plane area, an edge area or a corner area through semantic segmentation, applies normal vector perpendicularity constraint to the plane area, applies straightness constraint to the edge area, applies curvature constraint to the corner area, and judges whether the measurement is qualified or not by calculating a comprehensive error, so that the accuracy and reliability of a measurement result are ensured.