CN118502239B - An analytical learning control system for an electronic nose - Google Patents
An analytical learning control system for an electronic noseInfo
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- CN118502239B CN118502239B CN202410565957.4A CN202410565957A CN118502239B CN 118502239 B CN118502239 B CN 118502239B CN 202410565957 A CN202410565957 A CN 202410565957A CN 118502239 B CN118502239 B CN 118502239B
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
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Abstract
The invention discloses an analysis learning control system of an electronic nose, which belongs to the technical field of electronic nose control and comprises a factor analysis module and a detection control module, wherein the factor analysis module is used for analyzing a target electronic nose, determining all environmental influence factors, analyzing all environmental influence factors, determining all combined segmentation intervals corresponding to the electronic nose and corresponding error influence curves, the detection control module is used for detecting and controlling all target electronic noses, determining all target electronic noses as similar marks corresponding to all target electronic nose marks, selecting one target electronic nose from all target electronic noses of each similar mark as a standard electronic nose, acquiring detection data of all target electronic noses in real time, establishing a detection learning model, acquiring standard detection results of the standard electronic noses, correcting the detection data of the target electronic noses with the same similar marks as the standard electronic noses through the detection learning model, and acquiring detection correction data of all target electronic noses.
Description
Technical Field
The invention belongs to the technical field of electronic nose control, and particularly relates to an analysis learning control system of an electronic nose.
Background
As an intelligent device for simulating the olfactory system of mammals, the electronic nose technology has shown wide application prospects in various fields in recent years. The technology mainly comprises a sensor array, a signal processing system and a pattern recognition system, and can realize rapid and accurate detection of target gas or volatile compounds.
Since the first proposal of the electronic nose concept, this technology has attracted extensive attention in academia and industry. Through decades of development, the electronic nose technology has made remarkable progress and plays an important role in the fields of food safety, environmental monitoring, medical diagnosis and the like. For example, in the food safety field, electronic nose technology can achieve rapid determination of freshness, deterioration degree and adulteration of food by detecting volatile compounds in the food. In the field of environmental monitoring, the electronic nose technology can be used for monitoring harmful gases and pollutants in the air in real time, and provides scientific basis for environmental protection departments.
However, although the electronic nose technology has achieved some results, there are still many issues to be improved. Most of the existing electronic nose systems adopt traditional pattern recognition algorithms, and the algorithms often have poor effect when processing complex gas mixtures, so that accurate recognition of target gas is difficult to realize. In addition, existing systems are also poorly adapted to environmental changes, and once environmental conditions change, the performance of the system can be severely impacted.
The electronic nose control system comprises an air path unit and a circuit unit, wherein the air path unit comprises an air filtering device, an electromagnetic three-way valve, a container to be detected, a sensor chamber, a flowmeter and a vacuum pump, the circuit unit comprises a main control module, a sensor detection module, a control circuit module and a man-machine interaction module, the man-machine interaction module comprises an RS232 serial port, an LED prompt lamp, a display screen, a wireless module and a wireless terminal, the RS232 serial port, the LED prompt lamp and the display screen are all connected with a second output end of the main control module, and the RS232 serial port, the wireless module and the wireless terminal are sequentially connected through common circuits and electronic elements.
Based on the analysis learning control system, the invention provides an analysis learning control system of the electronic nose.
Disclosure of Invention
In order to solve the problems of the scheme, the invention provides an analysis learning control system of an electronic nose.
The aim of the invention can be achieved by the following technical scheme:
an analysis learning control system of an electronic nose comprises a factor analysis module and a detection control module;
The factor analysis module is used for analyzing the target electronic nose, determining all environmental influence factors, analyzing all the environmental influence factors, and determining all the combined segmentation intervals and the corresponding error influence curves corresponding to the electronic nose.
Further, the method for analyzing each environmental impact factor comprises the following steps:
the method comprises the steps of obtaining influence analysis data corresponding to all environment influence factors, analyzing the influence analysis data, determining factor intervals corresponding to the environment influence factors, splitting and combining the factor intervals corresponding to the environment influence factors to obtain combined segmented intervals, and generating corresponding error influence curves based on the combined segmented intervals.
Further, the method for splitting and combining the factor intervals of the environmental influence factors comprises the following steps:
step SA1, setting floating points in each factor interval;
Step SA2, setting reference point combinations according to the floating points, and determining point combinations to be selected according to the reference point combinations;
Step SA3, evaluating the reference point combination and the point combination to be selected through a preset judgment model to obtain a corresponding judgment value, combining the point combination to be selected with the judgment value of 0 with the reference point combination to obtain an initial combination interval;
Step SA4, determining a new point combination to be selected according to the initial combination interval, evaluating the reference point combination and the point combination to be selected in the initial combination interval through a judgment model to obtain a corresponding judgment value, combining the point combination to be selected with the judgment value of 0 with the initial combination interval to obtain a new initial combination interval;
step SA5, the step SA4 is circulated until no point combination to be selected with the judgment value of 0 exists, and the current initial combination interval is marked as a combination segmentation interval;
step SA6, the steps SA2 to SA5 are looped until no reference point combination exists, and each combination segmentation section is obtained.
Further, the expression of the judgment model is;
Wherein q is input data, and the output data is a judgment value of 1 or 0.
Further, the method for determining the environmental impact factor includes:
establishing a reference library, wherein the reference library is used for storing each piece of reference electronic nose information and each piece of reference factors corresponding to each piece of reference electronic nose information;
Obtaining target electronic nose information, analyzing the target electronic nose information and each reference electronic nose information in a reference library through a preset equivalent analysis model to obtain equivalent evaluation values between the target electronic nose information and each reference electronic nose information,
And determining the equivalent electronic noses according to the equivalent evaluation values, acquiring reference factors corresponding to the equivalent electronic noses, integrating the acquired reference factors to acquire initial factors of the target electronic noses, and performing simulation experiments on the initial factors to determine environmental influence factors of the target electronic noses.
Further, the expression of the equivalent analysis model is;
In the formula, s is target electronic nose information and reference electronic nose information, and output data is equivalent evaluation value 1 or 0.
The detection control module is used for carrying out detection control, determining each target electronic nose, acquiring target environment data of each target electronic nose, carrying out feature extraction on the target environment data according to each environment influence factor, acquiring target environment features, matching corresponding combined segmented intervals according to each target environment feature, and marking the same kind of mark corresponding to each target electronic nose with the same combined segmented interval;
acquiring detection data of each target electronic nose in real time, wherein the detection data comprises acquisition data and detection results;
establishing a corresponding detection learning model based on a deep learning network;
Inputting the standard detection result and the corresponding detection data of the standard electronic nose into a detection learning model, and correcting the detection data of the target electronic noses with the same type marks as the standard electronic noses through the detection learning model to obtain detection correction data of each target electronic nose.
Further, the method for acquiring the standard detection result comprises the following steps:
the method comprises the steps of obtaining detection data corresponding to a standard electronic nose, determining each gas to be selected, matching error influence curves corresponding to each gas to be selected according to a combination segmentation interval corresponding to the standard electronic nose, obtaining single detection results of each gas to be selected according to each error influence curve, identifying error values corresponding to each single detection result according to the error influence curves, and correcting each single detection result according to each single detection result and the error value to obtain the standard detection result.
Further, the method for determining the standard electronic nose comprises the following steps:
Acquiring detection data of each target electronic nose with the same type of mark, and identifying gas types and gas concentrations corresponding to each detection data;
counting the detection data of each target electronic nose through a gas counting template to obtain gas counting data of each gas electronic nose;
The gas species are labeled i, i=1, 2, & gt, n being a positive integer, the target electronic nose is labeled j, j=1, 2, & gt, m being a positive integer;
According to the formula Calculating gas evaluation values of corresponding gas types in each target electronic nose;
Wherein PAij is a gas evaluation value;
According to the formula Calculating the priority value of each target electronic nose;
wherein PUYj is a priority value;
and selecting the target electronic nose with the largest priority value as the standard electronic nose.
Compared with the prior art, the invention has the beneficial effects that:
Through the mutual cooperation between factor analysis module and the detection control module, realize carrying out analysis study control to the electronic nose in the region, through setting up the detection control module, realize the detection control to the electronic nose, improve the detection precision of electronic nose, simultaneously based on the detection condition of standard electronic nose, realize carrying out dynamic correction to each target electronic nose that has the same kind of label, under the prerequisite that realizes improving detection precision, avoid too much changing and repacking current electronic nose, improve resource utilization.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive effort to a person skilled in the art.
Fig. 1 is a functional block diagram of the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in FIG. 1, an analysis learning control system of an electronic nose comprises a factor analysis module and a detection control module;
the factor analysis module is used for analyzing all environmental influence factors, determining all environmental factors influencing the type of electronic nose, analyzing all environmental factors, and determining the influence of different environmental factor data on different types of gas detection, wherein the specific process is as follows:
A large amount of electronic nose historical detection data are obtained, the same electronic nose historical detection data are classified into one type, differences among all classifications are identified, factors of the type of electronic nose affected by the environment can be counted according to the differences, and all environmental influence factors are determined.
Determining corresponding influence analysis data, namely relevant influence condition data corresponding to the environmental influence factors, according to the environmental influence data;
Analyzing the influence analysis data, and determining a factor interval corresponding to the environmental influence factors, namely a data range in which the environmental influence factors possibly appear in practical application;
According to the difference of the electronic nose control parameters, an error influence curve of each single gas is generated in each combined section, namely, the horizontal axis is the detection error of each electronic nose control parameter under the environment, the vertical axis is the detection error of the electronic nose control parameter under the environment, statistical setting is carried out according to a large amount of data, the average value is used as a representative error, the environmental influence in the same combined section is the same, the error influence curve is generated for the single gas, therefore, when corresponding historical data are selected for analysis in the process, the gas can be classified according to the gas types, and the influence condition of different electronic nose control parameters of different gas types in the combined section is determined.
The method for splitting and combining the factor intervals corresponding to the environmental influence factors comprises the following steps:
Setting up a floating point in each factor interval, wherein the floating point is corresponding environmental influence factor data in the factor interval, determining the corresponding environmental influence factor data according to the position of the floating point in the factor interval, establishing a judging model for judging whether the influence of the environmental influence factor data combination corresponding to each floating point on gas detection analysis is the same as that of the previous environmental influence factor data combination, training and judging according to the detection result corresponding to the environmental influence factor data combination, and obtaining the corresponding detection result from the historical detection data or simulation experiment result of the electronic nose according to the environmental influence factor data without considering the influence of different control parameters of the electronic nose, and further finishing the training data for establishing the judging model, wherein the expression is that Wherein q is input data, namely the combination of environmental impact factor data corresponding to each floating point, and output data is a judgment value 1 or 0; the analysis results are different, namely the environmental impact factor data combination of the current floating point is different from the environmental impact factor data combination compared with the current floating point in terms of the environmental impact of detection and analysis;
Determining a point combination to be selected according to the reference point combination, wherein the point combination to be selected is the reference point combination, and the floating point is changed according to the sequence of the factor intervals to form a floating point combination which is different from the reference point combination but not in the combined subsection interval;
The method comprises the steps of obtaining a reference point combination, obtaining a corresponding judgment value by evaluating the reference point combination and the point combination to be selected through a judgment model, merging the point combination to be selected with the reference point combination with the judgment value of 0 to obtain an initial combination interval, determining a new point combination to be selected according to the initial combination interval, evaluating the reference point combination and the point combination to be selected in the initial combination interval through the judgment model to obtain the corresponding judgment value, merging the point combination to be selected with the judgment value of 0 with the initial combination interval, and so on until no point combination to be selected with the judgment value of 0 exists, and marking the current initial combination interval as a combination segmentation interval;
And redefining the reference point combination, and analogically, until no reference point combination exists, and obtaining each combined segment section.
In one embodiment, if the electronic nose is a new product of an enterprise, the electronic nose will have insufficient material analysis data, and is easy to generate leakage and missing items, even if the electronic nose is set by a professional according to experience of the electronic nose, the electronic nose can have the problems, if a large number of experimental simulations are performed, the electronic nose needs a large number of experimental simulations under the premise of lacking a guiding direction, the efficiency is reduced, and the electronic nose is difficult to realize in practical application, based on the electronic nose, the environment influence factors are determined by adopting the following method:
acquiring various electronic nose information such as a detection mode, an acquisition mode, a data processing mode, an equipment component and the like of the electronic nose, and marking the electronic nose as a target electronic nose for distinguishing;
Acquiring each currently mature electronic nose capable of determining environmental impact factors thereof, marking the corresponding electronic nose as a reference electronic nose, marking each environmental impact factor as a reference factor, acquiring electronic nose information of each reference electronic nose, marking the electronic nose information as reference electronic nose information, integrating each reference electronic nose information with each corresponding reference factor, and establishing a reference library;
According to simulation, a large number of electronic nose information comparison results are set, namely whether corresponding environmental impact factors are equivalently referred to or not, a large number of training sets are formed, namely whether the electronic nose information comparison results are equivalent to corresponding environmental impact factors or not is judged according to actual detection conditions, the electronic nose information comparison results can be evaluated by combining corresponding similarity, the electronic nose information comparison results are higher than a certain preset value and are regarded as being in accordance with equivalent conditions, otherwise, the electronic nose information comparison results are not in accordance with the preset value and are abnormal data, a corresponding equivalent analysis model is established based on an isolated forest algorithm, the electronic nose information comparison results can be regarded as being equivalent to normal data, otherwise, the electronic nose information is regarded as abnormal data, the output data is equivalent evaluation value 1 or 0, and the expression is that S is input data, namely target electronic nose information and reference electronic nose information, and the output data is equivalent evaluation value 1 or 0;
Analyzing the target electronic nose information and the reference electronic nose information in the reference library through an equivalent analysis model, marking the reference electronic nose with an equivalent evaluation value of 0 as an equivalent electronic nose, integrating the reference factors of the equivalent electronic noses, which is equivalent to union, and determining the initial factors of the target electronic noses;
And (3) performing simulation experiments on all initial factors, determining whether the initial factors are influenced by the initial factors, and determining environmental influence factors and corresponding influence analysis data of the target electronic nose, namely related simulation experiment data.
In other embodiments, for example, the prior art can implement setting of the corresponding combined segmentation interval and the corresponding error influence curve, and the prior art can be selected for application as required.
Through setting up factor analysis module, realize the intelligent analysis to the target electronic nose, confirm the influence condition of target electronic nose by each environmental impact factor, be convenient for follow-up according to actual environment condition detect the adjustment, improve gas detection precision.
The detection control module is used for carrying out detection control, determining each target electronic nose, namely each electronic nose which is of the same type and needs detection analysis, namely each electronic nose which is of the same type and is subjected to work influence, acquiring target environment data of each target electronic nose, namely environment data corresponding to detection gas, carrying out characteristic extraction on the target environment data according to each environment influence factor to obtain target environment characteristics, namely environment influence factor data corresponding to each environment influence factor, matching corresponding combined segmentation intervals according to each target environment characteristic, and selecting one target electronic nose from each target electronic nose of each similar mark as a standard electronic nose;
Acquiring detection data of each target electronic nose in real time, namely detecting according to an original working mode to acquire a corresponding detection result, wherein the detection data comprises acquisition data and detection results;
The method comprises the steps of establishing a corresponding detection learning model based on a deep learning network, establishing a corresponding training set through a manual mode to train, wherein the training set comprises input data and output data, the input data is detection data of a standard electronic nose, standard detection results and detection data of other target electronic noses with the same type of marks, the output data is detection correction data of the detection data of the other target electronic noses, namely, correction is carried out on the detection data with the same influence condition by utilizing the difference condition between the detection data of the standard electronic nose and the standard detection results, the detection results in the detection data are adjusted to obtain detection correction data, and training is carried out through the established training set, and the intelligent model after successful training is marked as a detection learning model.
Inputting the standard detection result and the corresponding detection data into a detection learning model, and correcting the detection data of the target electronic noses with the same type marks as the standard electronic noses through the detection learning model to obtain detection correction data of each target electronic nose.
The standard detection result acquisition method comprises the following steps:
acquiring corresponding detection data, namely detection data corresponding to the moment of the detection result of the analysis standard;
Determining possible gases in a target environment, marking the gases as gases to be selected, and matching error influence curves corresponding to the gases to be selected according to the combined segmented interval;
identifying the corresponding electronic nose control parameter with the smallest error in each error influence curve, and marking the corresponding electronic nose control parameter as a single gas parameter;
the method comprises the steps of adjusting a target electronic nose to a single gas parameter, detecting the gas to obtain a single detection result of the gas to be selected, repeating the steps, detecting other gases to be selected to obtain the single detection result of each gas to be selected, and identifying an error value corresponding to each single detection result according to an error influence curve;
And correcting each single detection result according to each single detection result and the error value to obtain a standard detection result.
Through setting up detection control module, realize the detection control to the electronic nose, improve the detection precision of electronic nose, simultaneously based on the detection condition of standard electronic nose, realize carrying out dynamic correction to each target electronic nose that has the same kind label, under the prerequisite that realizes improving detection precision, avoid too much changing and repacking current electronic nose, improve resource utilization.
The method for selecting one target electronic nose from all the target electronic noses marked in the same type as the standard electronic nose comprises the following steps:
Acquiring detection data of each target electronic nose with the same type of mark, identifying gas types and gas concentrations corresponding to the detection data, obtaining various gas types after finishing, setting a gas statistical template according to the various gas types, namely, a statistical template comprising the various gas types, and counting the corresponding gas concentrations, wherein if the target electronic nose does not comprise the gas types, the gas concentration is 0;
counting the detection data of each target electronic nose through a gas counting template to obtain gas counting data of each gas electronic nose;
The gas species are labeled i, i=1, 2, & gt, n being a positive integer, the target electronic nose is labeled j, j=1, 2, & gt, m being a positive integer;
According to the formula Calculating gas evaluation values of corresponding gas types in each target electronic nose;
Wherein PAij is a gas evaluation value;
According to the formula Calculating the priority value of each target electronic nose;
wherein PUYj is a priority value;
and selecting the target electronic nose with the largest priority value as the standard electronic nose.
The method for correcting each single detection result according to each single detection result and the error value comprises the following steps:
The method comprises the steps of obtaining the influence condition of different types of gases on a single detection result under the condition of single gas analysis, and then combining corresponding error values to adjust, and specifically, based on the steps, carrying out simulation experiments, determining a plurality of groups of training sets, wherein the training sets comprise input data and output data, the input data comprise detection standard results composed of corrected single detection results, namely, simulating various detection conditions to obtain the single detection results and the error values and the accurate detection results under the set simulation background, establishing a corresponding single analysis model based on a CNN network or a DNN network, analyzing the single analysis model through the established training sets, and analyzing the single analysis model after successful training to obtain the corresponding standard detection results. In other embodiments, other ways may be applied to perform the correction to obtain the corresponding standard detection result.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas which are obtained by acquiring a large amount of data and performing software simulation to obtain the closest actual situation, and preset parameters and preset thresholds in the formulas are set by a person skilled in the art according to the actual situation or are obtained by simulating a large amount of data.
An embodiment of the present application further provides a computer readable storage medium, on which a computer program is stored, where the computer program when executed by a processor implements a method for implementing any of the steps of the learning control system for electronic nose, and those skilled in the art will understand that implementing all or part of the above-described methods in the embodiments may be implemented by instructing relevant hardware by using the computer program, where the computer program may be stored in a non-volatile computer readable storage medium, and where the computer program when executed may include the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium provided by the present application and used in embodiments may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), enhanced SDRAM (ESDRAM), synchronous link (SYNCHLINK) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising one does not exclude the presence of other like elements in a process, apparatus, article, or method that comprises the element.
The foregoing description is only of the preferred embodiments of the present invention and is not intended to limit the scope of the invention, and all equivalent structures or equivalent processes using the teachings of the present invention and the accompanying drawings, or direct or indirect application in other related arts, are included in the scope of the present invention
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
For convenience of description, the above devices are described as being functionally divided into various units, respectively. Of course, the functionality of each of the units may be implemented in one or more of software and/or hardware when implementing the application, as will be appreciated by those skilled in the art(s) embodiments of the application may be provided as a method, system or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flowchart and/or block of the flowchart illustrations and/or block diagrams, and combinations of flowcharts and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111855757A (en) * | 2020-07-21 | 2020-10-30 | 广西壮族自治区亚热带作物研究所（广西亚热带农产品加工研究所） | An electronic nose-based method for identifying the old fragrance of Liupao tea |
| CN117522744A (en) * | 2023-11-07 | 2024-02-06 | 湖州师范学院 | Spectral quantification correction method based on supervised manifold learning |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112595352B (en) * | 2020-11-16 | 2023-03-31 | 歌尔微电子股份有限公司 | Calibration method, device and equipment of bone voiceprint sensor and readable storage medium |
| EP4272636A4 (en) * | 2020-12-30 | 2024-09-04 | Neurophet Inc. | MEDICAL IMAGE ANALYSIS METHOD, MEDICAL IMAGE ANALYSIS DEVICE, AND MEDICAL IMAGE ANALYSIS SYSTEM |
| CN115469256A (en) * | 2022-09-16 | 2022-12-13 | 广东省肇庆市质量计量监督检测所 | Electric performance detector calibrating device for paint-coated assembly line |
| CN117168623B (en) * | 2023-08-28 | 2024-04-12 | 南京诺源医疗器械有限公司 | Medical multi-point fusion temperature measurement system and method |
| CN117606573A (en) * | 2024-01-04 | 2024-02-27 | 中国计量大学 | An online calibration design for ultrasonic flowmeter based on random forest algorithm |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117522744A (en) * | 2023-11-07 | 2024-02-06 | 湖州师范学院 | Spectral quantification correction method based on supervised manifold learning |
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