30 results about "Experimental data processing" patented technology

The invention discloses a multi-workstation collaborative chemical experiment robot control system, and relates to the technical field of robot control. According to the system, through closed-loop collaboration of the chemical knowledge model module, the robot experiment execution module, the equipment management module and the multi-workstation collaborative scheduling module, intelligent design of an experiment process, full-process automatic execution, standardized integration of data and accurate optimization of parameters are realized, and the efficiency and repeatability of a chemical experiment are greatly improved; errors caused by manual intervention are reduced, the research and development process of chemicals is accelerated, meanwhile, the product performance standard-reaching rate is increased by means of a spectrum structure-activity relationship prediction model, efficient overall utilization of resources of multiple workstations is achieved, and the working efficiency is improved. The problems that in a traditional chemical experiment, cooperative scheduling of multiple workstations is not smooth, experiment process design depends on artificial experience, parameter adjustment lacks scientific data support, experiment data processing is fragmented, and product performance prediction precision is insufficient are solved.

The application discloses a double-machine clutching type testing device and method for mechanical loss measurement of a variable-frequency motor, which comprises two identical variable-frequency motors, a rotating speed measurement unit, a power measurement unit and a driving control unit, wherein any one of the variable-frequency motors is used as a driving motor, and the other one is a load motor and is always powered off during the whole testing process; the driving motor is separated from or connected with the load motor through a shaft coupling to realize no-load or load; when the driving motor is connected with the load motor, the input active power of the driving motor under different rotating speeds is measured through the rotating speed measurement unit and the power measurement unit; when the driving motor is separated from the load motor, the input active power of the driving motor under different rotating speeds is measured; and the change of the input active power measurement value of the driving motor under the same rotating speed and no-load operation is the mechanical loss of the corresponding rotating speed. The application has the advantages of relatively simple measurement process and experimental data processing process, high measurement efficiency and high measurement precision.

The utility model discloses a device for measuring two-dimensional magnetic induction intensity on an axial plane of a Helmholtz coil, and belongs to the technical field of electronic components. The device comprises a two-dimensional Hall probe, a stepping motor guide rail moving circuit and a high-precision voltage acquisition module, wherein the two-dimensional Hall probe consists of a two-dimensional Hall probe mounting base and two linear Hall sensors which are orthogonally mounted on the two-dimensional Hall probe mounting base; the stepping motor guide rail moving circuit and the high-precision voltage acquisition module are connected with the two-dimensional Hall probe; two ends of the high-precision voltage acquisition module are respectively connected with the two-dimensional Hall probe and the computer. According to the utility model, the symmetrical distribution characteristic of the Helmholtz coil magnetic field is utilized, the two-dimensional Hall probe is designed by using the two mutually orthogonal Hall sensors, and the three-dimensional space magnetic field distribution is reflected by measuring the two-dimensional magnetic field of the 1 / 4 axis plane of the Helmholtz coil; the high-precision voltage acquisition module is designed based on the single-chip microcomputer, so that measurement errors are effectively Compared with an existing device of the same type, measurement is easier and more convenient, experimental data processing is easier and more intelligent, and the expandability of the device is higher.

The invention relates to the technical field of nuclear fusion experiment data processing and numerical simulation, and discloses an automatic concurrent physical analysis method and system for a magnetic confinement fusion device, and the method comprises the steps: S1, receiving input parameters including a shot number list and a time slice list, and constructing a directed acyclic graph; s2, scheduling and executing based on the directed acyclic graph: S2.1, requesting diagnosis and balancing original diagnosis data from a plurality of data source interfaces in parallel; s2.2, converting the coordinate system of the obtained original diagnostic data into a unified normalized small-radius magnetic flux surface coordinate, and performing profile fitting to obtain profile data; s2.3, executing multi-physical coupling calculation with a dependency relationship to generate convergent plasma balance and profile information; s3, verifying the data in the process and performing grading processing on exceptions; s4, writing the final result and the intermediate result into an HDF5 file; and S5, recording full-link metadata and a running log from data acquisition to result output. According to the invention, one-button analysis is realized, and the analysis efficiency is improved.

This invention relates to an experimental data processing method, apparatus, AI analysis module, and computer equipment, belonging to the field of data processing. The method includes: segmenting multi-dimensional raw data by type and unifying the format; generating a similarity matrix based on temporal and spatial neighborhood information to mark abnormal fluctuation points; separating effective signals by matching a noise library with time-frequency features; extracting multi-layer features of basic statistics, time-series correlation, and trend changes; and dynamically selecting core features to update a tracking experimental model. The accompanying AI analysis module integrates hardware circuits for data segmentation, similarity calculation, anomaly marking, noise matching, feature extraction, and model updating, achieving full-process acceleration. This method significantly improves the efficiency of experimental data processing and the reliability of conclusions through multi-dimensional data compatible processing, accurate anomaly detection, multi-layer feature fusion, and adaptive model optimization, and is suitable for real-time analysis in various scenarios such as scientific research and industry.

The invention discloses a flow field reconstruction and PIV (particle image velocimetry) measurement method based on a physical information neural network, which belongs to the technical field of fluid mechanics, deep learning, computer vision and PIV crossing and comprises the steps of low-cost experimental system construction, experimental data processing and period identification, simulation synchronization and data set construction and physical information neural network flow field reconstruction. According to the invention, the problems of high cost and non-real time of the traditional PIV technology are solved, a novel low-cost solution is provided for flow field measurement, and the popularization and application of the PIV technology in basic scientific research and teaching are promoted.

The application provides a kind of space science experimental data online collaborative analysis method and system, it is related to scientific experimental data processing and analysis technical field.The method comprises: in response to the project creation request sent by the first device, create space science data online research project;In response to the analysis request of space science data online research project sent by the second device, according to the running environment information scheduling environment mirror and computing resources, create running environment;In response to the online analysis operation sent by the second device, based on the plurality of functional components configured by running environment, determine target sub-result, in response to the result update request sent by the second device, the target sub-result identification information is carried in result update request, according to the identification information of target sub-result, update target result.The application can quickly and accurately complete the online processing and analysis of complex scientific tasks in the scene of multi-person collaborative processing, efficiently utilize space science experimental data, effectively improve data processing and analysis efficiency.

The invention provides a full-scene interactive middle school physics virtual experiment teaching system. The system comprises a core experiment simulation module, the module utilizes a physical engine, a three-dimensional modeling technology, a virtual reality technology and an image and sound processing technology, a physical experiment process can be reproduced truly, a correct result can be provided for correct operation of students, and phenomena conforming to a real experiment can be presented for wrong operation; and a matched experimental data processing module supports acquisition, analysis and result presentation of experimental data. The system also integrates comprehensive teaching management functions of user management, course management, experiment management, teaching condition statistics, authority management and the like. On the basis, students can carry out high-quality experiments in a safe and convenient virtual environment, and understanding of physical theories is deepened through operation by themselves; meanwhile, the dependence of schools on entity laboratories and equipment is effectively reduced, the utilization efficiency of teaching resources and the flexibility of teaching are improved, and a powerful auxiliary tool is provided for physics teaching.

This invention claims protection for an improved wavelet thresholding denoising method. The method decomposes a noisy signal through several layers of wavelet decomposition, obtaining the corresponding high-frequency wavelet coefficients for each layer. The amplitude of the corresponding wavelet coefficients is analyzed; those for useful signals are lower, while those for noise are higher. An improved threshold function is applied to process the wavelet coefficients at each layer, retaining coefficients below the threshold and discarding those above. These wavelet coefficients are then reconstructed using inverse wavelet transform, outputting the denoised signal. This invention constructs a new threshold function to address the shortcomings of traditional wavelet denoising algorithms in noise reduction. Experimental data processing results show that the new threshold function effectively suppresses the influence of random errors in the inertial measurement unit. Compared to the original compromise threshold algorithm, the signal-to-noise ratio is improved, the root mean square error is reduced, and the scheme of this invention achieves better signal smoothness and excellent denoising effect compared to traditional denoising schemes.

The application discloses a fireproof mortar pyrolysis reaction mechanism obtaining method and system based on a multi-peak pyrolysis curve, and comprises the following steps: S1, collecting non-isothermal pyrolysis experimental data of the fireproof mortar, i.e. thermogravimetric experimental data; S2, processing the thermogravimetric data to obtain a differential thermogravimetric curve of the fireproof mortar, and determining the number of reaction peaks; S3, fitting the differential thermogravimetric curve based on a deconvolution method to obtain a sub-reaction peak; S4, optimizing the sub-reaction peak by using a particle swarm algorithm to determine the combined kinetic parameters thereof; and S5, obtaining the pyrolysis reaction mechanism and kinetic parameters of each reaction peak. The application processes the multi-peak pyrolysis conversion rate data of the fireproof mortar by using the deconvolution method, overcomes the difficulty in obtaining the pyrolysis mechanism function of the sub-reaction peak by using the combined kinetics and the particle swarm algorithm, solves the problem that the mechanism function needs to be assumed in the traditional method, and obtains the reaction mechanism function based on the combined kinetics, which is more suitable for the actual pyrolysis process of the fireproof mortar.

The application discloses a kind of ring upsetting friction coefficient determination methods based on image recognition, belongs to upsetting friction coefficient determination technical field, S1: the image of ring sample after upsetting is collected;S2: the inner contour of ring sample after upsetting in image is extracted by image edge extraction method, and the edge contour is further extracted using subpixel edge extraction method to inner contour;The inner diameter of ring after upsetting represented by edge contour is obtained;S3: based on standard friction coefficient theory calibration curve, the inner diameter of ring after upsetting, original inner diameter before upsetting, original height before upsetting and target height after upsetting are used to calculate the friction coefficient after upsetting.The application can improve detection efficiency on the basis of ensuring measurement accuracy through two rounds of edge feature extraction, thereby solving the time-consuming and laborious problem in experimental data processing when measuring friction coefficient by ring upsetting method.

The invention discloses a five-hole probe calibration and data processing method based on deep learning, relates to experimental measurement of aerodynamic performance of a gas turbine, and is used for quickly and accurately processing data acquired by a five-hole probe in the gas turbine. The method comprises the following steps: carrying out a five-hole probe calibration experiment; constructing a five-hole probe neural network dimensionless calibration data set; constructing and training a five-hole probe neural network; carrying out turbine blade grid pneumatic experiment and five-hole probe experiment data acquisition; experimental data processing based on a neural network; and inverting aerodynamic parameters. The internal flow of a gas turbine is complicated and accompanied by a large airflow angle and a strong pressure gradient, fine measurement is often adopted for fully capturing flow characteristics, and the five-hole probe calibration and data processing method based on deep learning is constructed to be used for realizing rapid and high-applicability five-hole probe data processing.

The invention relates to the field of aero-engine heat transfer and mass transfer analysis, and discloses a method and a system for processing cooling effect heat exchange experimental data under a curved surface structure of an aero-engine. The method comprises the following steps: expanding a whole curved surface cloud picture into a two-dimensional image containing real arc length coordinate points, reconstructing the expanded two-dimensional image into a grid matrix, performing identification assignment operation on the grid matrix by using a respe function, and accurately mapping physical quantity data to corresponding positions of the grid matrix, thereby forming a two-dimensional image type grid cloud picture. According to the method, data are displayed in a more intuitive and ordered manner, a polygonal region is drawn to select an invalid data range, and a createMask function is used to generate a binary mask region, so that the range of invalid data is clearly identified, corresponding physical quantity data is marked as invalid data, and interference is eliminated for subsequent deep analysis of valid data.

This invention relates to the fields of structural engineering experimental data processing and machine learning technology, and discloses a hysteresis curve parameter recognition method based on machine vision phenomenology. It obtains initial estimates of hysteresis model parameters using traditional parameter recognition methods. These initial estimates serve as a benchmark for applying physical constraints to perform adaptive intelligent sampling in the parameter space, generating multiple parameter combinations. The force-displacement curves corresponding to each parameter combination are converted into standard grayscale images. A Siamese convolutional neural network model is constructed and trained to learn the mapping relationship between image similarity and curve fitting goodness. The similarity between the generated image and the original experimental image is evaluated using the trained model, and the parameter combination corresponding to the highest score is selected. Further multi-layer physical rationality verification is then performed to select the optimal hysteresis model parameters. This invention integrates the physical basis of traditional methods with the visual shape recognition capabilities of CNNs, significantly improving parameter recognition accuracy and robustness while ensuring that the recognition results conform to physical laws.

The invention discloses a digital processing method and system for Zeeman effect experiment data, and relates to the technical field of atomic physics experiment data processing, and the method comprises the following steps: S1, image reading and preprocessing: obtaining a Zeeman effect interference image, carrying out the self-adaptive zooming according to the image size, converting the Zeeman effect interference image into a gray image, and carrying out the self-adaptive zooming according to the image size; enhancing the contrast ratio through self-adaptive histogram equalization, and removing salt and pepper noise by adopting 3 * 3 median filtering; s2, interference ring identification and measurement: carrying out edge detection by adopting a Sobel operator, detecting an interference ring in the image through Hough transform, screening two maximum concentric circles closest to the center of the image, automatically calculating the diameter of the ring, and converting the diameter of the ring into a physical size; s3, key physical quantity calculation: calculating a wavenumber difference, substituting the wavenumber difference into the optimized formula to calculate an electron charge-to-mass ratio, and performing error propagation analysis. Through automatic image processing and a precise algorithm, manual measurement errors are avoided, and the relative error of the electron charge-to-mass ratio can be reduced to 1.07% or below.

The invention provides an online collaborative analysis method and system for space science experiment data, and relates to the technical field of scientific experiment data processing and analysis. The method comprises the following steps: in response to a project creation request sent by first equipment, creating a space science data online research project; in response to an analysis request of the space science data online research project sent by the second equipment, scheduling an environment mirror image and computing resources according to the operating environment information, and creating an operating environment; and in response to an online analysis operation sent by the second equipment, determining a target sub-result based on the plurality of functional components configured in the operating environment, in response to a result updating request sent by the second equipment, the result updating request carrying identification information of the target sub-result, and updating the target result according to the identification information of the target sub-result. According to the method, online processing and analysis of complex scientific tasks can be rapidly and accurately completed in a multi-person cooperative processing scene, space scientific experiment data is efficiently utilized, and the data processing and analysis efficiency is effectively improved.

Embodiments of the present application disclose an experimental data processing method and device; after obtaining an experimental index data set, according to the experimental grouping and data type of the experimental index data in the experimental index data set, an experimental data set to be processed for the experimental index is constructed, then, based on a preset individual difference parameter, a difference regression coefficient of each experimental index is identified in the experimental data set to be processed, panel experimental data is screened out in the experimental data set to be processed, and based on the panel experimental data, the variance of the difference regression coefficient is calculated to obtain difference detection data of each experimental index, then, when receiving a difference detection request for a target experimental index, difference detection is performed on the target experimental index according to the difference detection data, and the detection result is sent to a terminal; the scheme can improve the processing efficiency of experimental data processing.

This invention specifically relates to a method for testing and analyzing the noise of a mechanical hydraulic steering system. The method includes: S1, placing microphones at the driver's inner and outer ears in the vehicle under test, simultaneously reading the engine speed, and measuring the in-vehicle noise level and corresponding engine speed under typical operating conditions; S2, warming up the vehicle's engine; S3, measuring typical noise under idling conditions; S4, measuring typical noise under driving conditions; S5, analyzing the measured data according to experimental data processing methods to determine whether noise problems exist. This invention describes a method for testing and analyzing typical NVH problems that may exist in mechanical hydraulic steering systems under vehicle conditions, proposing a method for testing and analyzing the noise of mechanical hydraulic steering systems, and solving the problem of insufficient testing methods in this field for the high-quality development of commercial vehicles.

This paper relates to the field of automated data processing and provides an experimental data processing method, apparatus, and system. The method includes: sending a task preparation sequence instruction to a data computing system, causing the data computing system to create task-related computing processes according to the task preparation sequence instruction, create data objects within the computing processes, and assign initial values; sending a data acquisition instruction to a device control terminal, receiving data acquired by the device control terminal through operation of the experimental equipment; generating a data modification instruction based on the acquired data and sending it to the data computing system; sending a data calculation instruction to the data computing system, causing the data computing system to perform calculations on the data of the relevant task computing processes according to the data calculation instruction; and sending a data output instruction to the data computing system, causing the data of the relevant computing processes to be stored in a data object database according to the data output instruction. This paper can realize the entire business process and has the advantages of ease of use, ease of learning, and versatility.

This application discloses a method, apparatus, device, and storage medium for high-resolution phase information extraction. The method includes: deconstructing and separating overlapping positive and negative phase images of a sample using a deep convolutional neural network model based on an attention mechanism; establishing a differential phase image based on the deconstructed and separated positive and negative phase images; generating network training data using an X-ray phase difference microscopy physical model; and directly using the trained model for experimental data processing to extract the absolute and differential phase information of the sample. The above-mentioned solution provided by this application can quickly and accurately extract the absolute and differential phase information of a sample from overlapping positive and negative X-ray phase microscopy data. Furthermore, this invention uses numerical simulation to generate network training data, eliminating the need to collect training data on an experimental system, greatly reducing experimental difficulty and saving experimental time.

A method and system for image recognition of stripe spacing in liquid crystal textures, relating to the field of liquid crystal texture experimental data processing technology, includes: segmenting and grayscale processing a liquid crystal texture image to obtain multiple sub-images; calculating the gradient of each sub-image to obtain image gradient information; transforming the gradient direction matrix by treating gradient directions differing by 180° as the same direction to obtain a transformed direction matrix; statistically analyzing the gradient directions corresponding to gradients greater than a first multiple of the maximum gradient value in the gradient magnitude matrix in the transformed direction matrix to obtain an directional gradient histogram; and, when determining that stripes in a sub-image have a main direction, dividing multiple horizontal sampling lines and extracting their grayscale data signals to calculate the corresponding stripe spacing. This application only statistically analyzes the gradient directions corresponding to gradients greater than a first multiple of the maximum gradient value in the gradient magnitude matrix to obtain the directional gradient histogram, which can filter noise, eliminate brightness unevenness, and improve the accuracy of image recognition results.

The embodiment of the invention discloses an experimental data processing method, system and device, equipment and a medium. The method comprises the steps of obtaining a data set corresponding to an experiment for business development, wherein the data set comprises experiment observation data and an experiment index statistical result obtained based on the experiment observation data; executing the following steps through the language model: calling a data verification service, generating a data verification result aiming at the experimental observation data, and calling a result verification service, and generating a result verification result aiming at the experimental index statistical result; calling a causal analysis service, and generating a causal analysis result based on the data verification result and the result verification result; and generating an experiment analysis result of the experiment based on the data verification result, the result verification result and the causal analysis result. According to the scheme, the efficiency, accuracy, flexibility and the like of experimental analysis are improved, and the reliability of experimental data processing is high.

The present application belongs to the technical field of marine surveying and mapping, and is a surface current prediction method based on an attention mechanism TCN-LSTM model, comprising the following steps: (1) collecting real surface current data through a data acquisition module, (2) processing surface current data using an experimental data processing module, (3) constructing an attention mechanism TCN-LSTM surface current prediction model using a model building module, (4) verifying and testing the surface current data, and (5) predicting the neural network model. The present application method utilizes the characteristics of long short-term memory networks and the advantages of time convolution, and simultaneously adds an attention mechanism, and predicts after processing real historical marine data of a certain sea area measured by a WaMoSII system. The method proposed by the present application has a surface current flow velocity accuracy of 92.9% and a flow direction accuracy of 90.4%.

The application discloses an intelligent auxiliary system for medical scientific research training based on an artificial intelligence large model, and particularly relates to the field of artificial intelligence and scientific research data processing, and is used for solving the problems of multi-source dispersion, non-uniform structure, missing steps difficult to identify and dependence on manual verification of experimental records in the existing medical scientific research process; a digital experimental record file is constructed by image acquisition on an experimental table record carrier and in combination with target detection and text recognition, logical breakpoints are recognized based on numerical jump and semantic co-occurrence, and jump record recognition is realized by using phenotype feature distribution and optimal transport calculation; missing steps are further reasoned and completed by dynamic programming, and finally, a complete experimental process draft is generated in combination with a structured template, automatic analysis, abnormal identification, process completion and multi-modal data fusion processing of scientific research data are realized, and the intelligent level of experimental data processing and the standardization of the scientific research process are improved.

The application provides a medical experiment data processing method and system, which realizes intelligent integration and efficient processing of multi-modal medical experiment data by introducing an adaptive mixed model, has the characteristics of data preprocessing automation, multi-dimensional feature extraction, adaptive model selection and strong visual interaction. The system can automatically identify and adapt to different types of data processing algorithms, effectively improving the accuracy and efficiency of data processing; through multi-modal feature fusion and time alignment technology, cross-data source correlation analysis is realized; at the same time, user-defined view and real-time feedback optimization are supported, which significantly enhances the practicability and flexibility of the system, provides comprehensive, intuitive and efficient data analysis support for medical researchers, and effectively promotes the deep mining and scientific discovery of medical experiment data.

The embodiment of the application belongs to the field of big data, and relates to an AB experiment data processing method, comprising the following steps: acquiring AB experiment data through a spring service; adding attribute labels to candidate events in the AB experiment data according to an attribute list configured in a TiDB database to obtain to-be-processed events; storing the to-be-processed events in a clickhouse table of a clickhouse database; and executing a preset SQL task to perform data processing on the clickhouse table to obtain an AB experiment data processing result, wherein the attribute labels are used to extract data in the to-be-processed events. The application further provides an AB experiment data processing device, a computer device and a storage medium. In addition, the application also relates to blockchain technology, and the AB experiment data can be stored in a blockchain. The application improves the processing efficiency of AB experiment data.

The application relates to a method for exploring the vibration response of the floor of the cab of a wheel excavator based on Hyper Mesh simulation, and belongs to the technical field of related parameter testing of engineering machinery. In the method, an acceleration sensor is arranged on the floor of the cab of the wheel excavator, and meanwhile, all experimental equipment is arranged; then, the wheel excavator is started to begin testing, the vibration signal of the floor of the cab under an idling condition is measured, and after the experimental data are processed, the vibration acceleration spectrum diagram of the floor of the cab is obtained through drawing software; a three-dimensional model of the cab with the same size as the appearance size of the actual vehicle is established, is simplified and is imported into Hyper Mesh software to perform geometric cleaning; after the three-dimensional model of the cab is processed, the finite element model of the cab is obtained, the finite element model is calculated, and finally, the structural modal vibration mode diagram of the cab is obtained; by comparing the vibration signals of the floor of the cab in the actual vehicle test and the Hyper Mesh simulation, the accurate vibration response of the floor can be determined.

The present invention discloses an automobile road test system based on virtual reality and road test data processing, comprising an experimental operating table, a server, and a driving simulator. The driving simulator comprises a console, a base, and a housing. The housing has an inner cavity provided with a cockpit. A rotating assembly is symmetrically fixedly connected between the top of the base and the bottom of the housing. A cylinder is rotatably sleeved in the inner cavity of the rotating assembly. A tilting mechanism is symmetrically provided at the bottom of the inner cavity of the housing. An impact device is symmetrically provided on the surface of the housing. A rotating door is rotatably connected to the side of the housing via a hinge. The present invention relates to the field of automobile testing technology. This automobile road test system based on virtual reality and road test data processing solves the problems of difficulty in simulating the driving experience of a real vehicle, the poor match between test results and the driving process, the inability to effectively train test personnel, and the inability to feel the impact caused by the vehicle colliding with the road during simulated road driving.

This disclosure provides an experimental data processing method, apparatus, storage medium, and electronic device. The method includes acquiring experimental group service data of a target strategy; extracting a preset identifier from the experimental group service data; and generating first experimental data based on the preset identifier; wherein the preset identifier indicates whether the target solution determined by the target strategy is transmitted to the user terminal for execution. By identifying the preset identifier in the experimental group service data, it can be determined whether the target strategy reaches the user, i.e., whether the target solution determined by the target strategy is transmitted to the user terminal corresponding to the experimental group service data for execution. Generating the first experimental data based on the experimental group service data that reaches the user reduces interference from experimental group service data that cannot reach the user, thus improving the accuracy of the first experimental data.

The invention provides an experimental data processing method, device and system, electronic equipment and a storage medium. The method comprises the following steps: creating a target experimental process; determining a target experiment step, and retrieving and searching a target formula script; obtaining experimental data; and calling the target formula script, and calculating the experimental data to obtain the experimental data of the target experimental step. Compared with the prior art, the embodiment of the invention has the advantages that the target formula script is quickly retrieved and called through the preset formula library, so that the tedious process of manually compiling or searching the formula is avoided, and the execution efficiency of the experiment is improved; automatic calculation reduces the possibility of human errors, so that experimental data are more accurate and reliable; a uniform formula script is used for calculation, so that the consistency of data calculation among different experiment steps is ensured, and experiment results have higher comparability and repeatability; a preset formula library can be updated and expanded according to needs so as to adapt to new experiment requirements or improve a calculation method.