CN111157584A - Alcohol content detection method, device and detection equipment - Google Patents
Alcohol content detection method, device and detection equipment Download PDFInfo
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Abstract
The invention provides a method, a device and equipment for detecting alcohol content, wherein the method comprises the steps of applying an excitation signal to a sample to be detected; determining response data of the sample to be tested based on the excitation signal; determining the alcohol content in the sample to be tested by combining a pre-trained neural network model according to the response data; the neural network model has learned the correspondence between each response data and the alcohol content. The method and the device can effectively improve the accuracy of alcohol content detection and improve the alcohol content detection effect.
Description
Technical Field
The invention relates to the technical field of wine detection, in particular to a method, a device and equipment for detecting alcohol content.
Background
In some application scenarios, it is usually necessary to measure the alcohol content of the wine, for example, by manual sensory evaluation, or by measuring the content of substance components in the wine by some physical and chemical phenomena, so as to determine the alcohol content, or by rough measurement of the alcohol content by spreading the alcohol on a filter paper.
In these ways, the accuracy of alcohol content detection is not high, and the alcohol content detection effect is not good.
Disclosure of Invention
The present invention is directed to solving, at least to some extent, one of the technical problems in the related art.
Therefore, the invention aims to provide an alcohol content detection method, an alcohol content detection device and alcohol content detection equipment, which can effectively improve the alcohol content detection accuracy and improve the alcohol content detection effect.
In order to achieve the above object, an embodiment of the invention provides a method for detecting an alcohol content, including: applying an excitation signal to a sample to be tested; determining response data of the sample to be tested based on the excitation signal; determining the alcohol content in the sample to be tested by combining a pre-trained neural network model according to the response data; the neural network model has learned the correspondence between each response data and the alcohol content.
According to the method for detecting the alcohol content, provided by the embodiment of the first aspect of the invention, the excitation signal is applied to the sample to be detected, the response data of the sample to be detected based on the excitation signal is determined, and the alcohol content in the sample to be detected is determined according to the response data and in combination with the pre-trained neural network model; the neural network model learns the corresponding relation between each response data and the alcohol content, so that the alcohol content detection accuracy can be effectively improved, and the alcohol content detection effect is improved.
In order to achieve the above object, an alcohol content detecting device according to an embodiment of the second aspect of the present invention includes: the signal transmitting module is used for applying an excitation signal to a sample to be detected; a first determination module for determining response data of the sample to be tested based on the excitation signal; the second determination module is used for determining the alcohol content in the sample to be tested by combining a pre-trained neural network model according to the response data; the neural network model has learned the correspondence between each response data and the alcohol content.
The alcohol content detection device provided by the embodiment of the second aspect of the invention applies the excitation signal to the sample to be detected, determines the response data of the sample to be detected based on the excitation signal, and determines the alcohol content in the sample to be detected by combining the pre-trained neural network model according to the response data; the neural network model learns the corresponding relation between each response data and the alcohol content, so that the alcohol content detection accuracy can be effectively improved, and the alcohol content detection effect is improved.
In order to achieve the above object, an alcohol content detecting apparatus according to an embodiment of a third aspect of the present invention includes: the second aspect of the invention provides an alcohol content detecting device.
The detection device provided by the embodiment of the third aspect of the invention applies an excitation signal to a sample to be detected, determines response data of the sample to be detected based on the excitation signal, and determines the alcohol content in the sample to be detected by combining a pre-trained neural network model according to the response data; the neural network model learns the corresponding relation between each response data and the alcohol content, so that the alcohol content detection accuracy can be effectively improved, and the alcohol content detection effect is improved.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The foregoing and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a schematic flow chart of a method for detecting alcohol content according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating a method for detecting alcohol content according to another embodiment of the present invention;
FIG. 3 is a diagram illustrating impedance values according to an embodiment of the present invention;
FIG. 4 is a diagram illustrating a training result according to an embodiment of the present invention;
FIG. 5 is a diagram illustrating another training result according to an embodiment of the present invention;
FIG. 6 is a diagram illustrating another training result according to an embodiment of the present invention;
FIG. 7 is a diagram illustrating another training result according to an embodiment of the present invention;
fig. 8 is a schematic structural diagram of an alcohol content detecting apparatus according to an embodiment of the present invention;
FIG. 9 is a schematic structural diagram of an alcohol content detecting apparatus according to another embodiment of the present invention;
fig. 10 is a schematic structural diagram of a detection apparatus according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention. On the contrary, the embodiments of the invention include all changes, modifications and equivalents coming within the spirit and terms of the claims appended hereto.
In order to solve the technical problems of low accuracy and poor alcohol content detection effect in the related art, the embodiment of the invention provides an alcohol content detection method, which comprises the steps of applying an excitation signal to a sample to be detected, determining response data of the sample to be detected based on the excitation signal, and determining the alcohol content in the sample to be detected by combining a pre-trained neural network model according to the response data; the neural network model learns the corresponding relation between each response data and the alcohol content, so that the alcohol content detection accuracy can be effectively improved, and the alcohol content detection effect is improved.
Fig. 1 is a schematic flow chart of a method for detecting alcohol content according to an embodiment of the present invention.
Referring to fig. 1, the method includes:
s101: an excitation signal is applied to the sample to be tested.
Alternatively, a single electrode may be used to apply a variable frequency excitation signal to the sample to be tested.
In the embodiment of the present invention, a single interdigital electrode of 20 pairs of electrodes can be specifically used as the single electrode, and the electrode plating layer is Au/Ni/Cu, which is not limited herein.
In the specific implementation process, the single electrode can specifically adopt a single 20-pair interdigital electrode, the electrode coating is Au/Ni/Cu, and the single 20-pair interdigital electrode is used to apply a variable-frequency excitation signal to the sample to be tested, for example, byThe CHI660E electrochemical workstation applies a disturbance voltage of 5mV and a frequency range of 0.1Hz to 10 mV to a sample to be tested through a single interdigital electrode with 20 pairs of electrodes5And Hz frequency conversion excitation signals, thereby assisting in obtaining response data of the sample to be detected to the frequency conversion excitation signals subsequently.
In the embodiment of the invention, the alcohol content is detected by adopting the single electrode to drive, so that the detection equipment is easy to maintain, and meanwhile, the hardware cost of the equipment required for detection can be effectively reduced.
S102: and determining response data of the sample to be tested based on the excitation signal.
In the specific implementation process, the electrical parameters related to the response signals of the sample to be tested based on the variable frequency excitation signals with different frequencies can be obtained and used as the response data, and the related electrical parameters are, for example, voltage or resistance, and the like, and the method is not limited thereto.
In the embodiment of the present invention, the impedance value of the sample to be measured based on the variable frequency excitation signals with different frequencies may be used as the response data, and more specifically, the impedance value may include: the real part and the imaginary part of the impedance are used as response data, so that the detection is simple, convenient and easy to obtain, and the electrochemical characteristics of samples to be detected with different alcohol contents can be effectively presented.
S103: determining the alcohol content in the sample to be tested by combining a pre-trained neural network model according to the response data; the neural network model has learned the correspondence between each response data and the alcohol content.
Due to the pre-trained neural network model, a mapping relationship between the response data of the initial sample and the actual alcohol content has been learned. Therefore, response data of a sample to be detected based on the excitation signal can be input into the pre-trained neural network model, and the response data is identified by adopting the pre-trained neural network model, so that the alcohol content corresponding to the response data is identified as the alcohol content obtained by detection, the aim of identifying the alcohol content based on artificial intelligence is fulfilled, and the accuracy of alcohol content detection is improved.
In the embodiment, an excitation signal is applied to a sample to be detected, response data of the sample to be detected based on the excitation signal is determined, and the alcohol content in the sample to be detected is determined according to the response data by combining a pre-trained neural network model; the neural network model learns the corresponding relation between each response data and the alcohol content, so that the alcohol content detection accuracy can be effectively improved, and the alcohol content detection effect is improved.
Fig. 2 is a schematic flow chart of a method for detecting alcohol content according to another embodiment of the present invention.
Referring to fig. 2, the method includes:
s201: carbon dioxide in the initial sample is removed prior to training the neural network model.
Wherein the initial sample in S201 may be a sample containing a known alcohol content.
In a specific implementation, the carbon dioxide in the initial sample is removed prior to training the neural network model.
S202: when the neural network model is trained, a set amount of alcohol is added to the initial sample after carbon dioxide removal to obtain a plurality of initial samples with different alcohol contents, and the obtained plurality of different alcohol contents are used as the alcohol contents marked by the corresponding initial samples.
In the specific implementation process, when training the neural network model, a set amount of alcohol is added to the initial sample after carbon dioxide is removed, and after each time the set amount of alcohol is added, response data corresponding to the initial sample after alcohol is added is detected, and the detection of the response data refers to the above embodiment.
The initial sample obtained each time a set amount of alcohol is added may be based on the impedance values of the variable frequency excitation signal at different frequencies, as response data, and more specifically, the impedance values may include: the real and imaginary parts of the impedance are determined by taking the impedance values as response data.
Referring to fig. 3, fig. 3 is a schematic diagram of impedance values in the embodiment of the present invention, including: the impedance values may include: in this embodiment, an ac impedance spectrum may be formed according to the real part and the imaginary part of the impedance, the real part and the imaginary part of the impedance are presented in the form of a spatial rectangular coordinate axis, an x coordinate is taken as a frequency, a y coordinate is taken as a real part Z' of the impedance, and a Z coordinate is taken as an imaginary part of the impedance for example, referring to fig. 3, each line describes a corresponding relationship between the alcohol content and the real part and the imaginary part of the corresponding impedance, and different lines represent initial samples with different alcohol contents.
As an example, the initial sample is shaken sufficiently and ultrasonically treated for 5 minutes to remove dissolved carbon dioxide gas, 25mL is measured, response data of the initial sample is measured, then 0.2mL of alcohol with a set amount is added, the actual alcohol content of the initial sample after the alcohol with the set amount is added is recorded as the labeled alcohol content, the response data of the initial sample is measured, then the alcohol with the set amount of 0.2mL is added for testing again, the measurement on a single initial sample is repeated for 4 times to reduce errors, and the surface of the interdigital electrode needs to be cleaned before each measurement, so that the detection accuracy is guaranteed.
S203: and (4) performing iterative training on the neural network model by adopting a plurality of initial samples with labeled alcohol content.
After the initial samples with different alcohol contents are determined and corresponding response data are recorded for each initial sample, the neural network model can be iteratively trained by adopting the initial samples with the labeled alcohol contents.
Optionally, the neural network model is trained by using initial samples with each response data, and the training of the neural network model is completed until the alcohol content of the initial samples identified by the neural network model matches with the alcohol content labeled in advance.
Referring to fig. 4, 5, 6, and 7, fig. 4 is a schematic diagram of a training result according to an embodiment of the present invention. Fig. 4 shows a comparison result between the alcohol content of the initial sample identified by the neural network model and the alcohol content labeled in advance in one training process, and fig. 5 is a schematic diagram of another training result in the embodiment of the present invention. Fig. 5 shows a regression result of the neural network model, and fig. 6 is a schematic diagram of another training result in the embodiment of the present invention. Figure 6 shows the performance verification of the neural network model after four iterative training, with different lines representing different error performance. FIG. 7 is a diagram illustrating another training result according to an embodiment of the present invention. Figure 7 shows a neural network training state.
Removing carbon dioxide in an initial sample before training a neural network model; when a neural network model is trained, gradually adding a set amount of alcohol to the initial sample from which carbon dioxide is removed to obtain a plurality of initial samples with different alcohol contents, and taking the obtained plurality of different alcohol contents as the alcohol contents marked by the corresponding initial samples; the neural network model is subjected to iterative training by adopting a plurality of initial samples with labeled alcohol content, so that the capacity of the neural network model for identifying the alcohol content can be effectively improved, and the alcohol content detection effect is improved.
S204: an excitation signal is applied to the sample to be tested.
S205: and determining response data of the sample to be tested based on the excitation signal.
S206: determining the alcohol content in the sample to be tested by combining a pre-trained neural network model according to the response data; the neural network model has learned the correspondence between each response data and the alcohol content.
S204-S206 can refer to the above embodiments, and are not described herein.
In the embodiment, an excitation signal is applied to a sample to be detected, response data of the sample to be detected based on the excitation signal is determined, and the alcohol content in the sample to be detected is determined according to the response data by combining a pre-trained neural network model; the neural network model learns the corresponding relation between each response data and the alcohol content, so that the alcohol content detection accuracy can be effectively improved, and the alcohol content detection effect is improved.
Fig. 8 is a schematic structural diagram of an alcohol content detecting apparatus according to an embodiment of the present invention.
Referring to fig. 8, the apparatus 800 includes:
a signal transmitting module 801, configured to apply an excitation signal to a sample to be tested;
a first determining module 802, configured to determine response data of the sample to be tested based on the excitation signal;
a second determining module 803, configured to determine, according to the response data, the alcohol content in the sample to be tested in combination with the pre-trained neural network model; the neural network model has learned the correspondence between each response data and the alcohol content.
Optionally, in some embodiments, the neural network model is trained by using initial samples with respective response data, and the training of the neural network model is completed until the alcohol content of the initial samples identified by the neural network model matches the pre-labeled alcohol content.
Optionally, in some embodiments, the signal transmitting module 801 is specifically configured to:
and applying a variable-frequency excitation signal to the sample to be tested by adopting a single electrode.
Optionally, in some embodiments, the response data is impedance values of the sample to be tested at different frequencies of the excitation signal.
Optionally, in some embodiments, referring to fig. 9, the apparatus 800 further comprises:
the processing module 804 is configured to perform removal processing on carbon dioxide in the initial sample before training the neural network model, and successively add a set amount of alcohol to the initial sample after the carbon dioxide is removed when training the neural network model to obtain a plurality of initial samples with different alcohol contents, and use the obtained plurality of different alcohol contents as the alcohol contents marked by the corresponding initial samples;
and a training module 805, configured to perform iterative training on the neural network model by using a plurality of initial samples with labeled alcohol content.
Optionally, in some embodiments, the single-branched electrodes are interdigitated electrodes.
It should be noted that the explanation of the embodiment of the alcohol content detecting method in the foregoing embodiments of fig. 1 to fig. 7 also applies to the alcohol content detecting apparatus 800 of this embodiment, and the implementation principle is similar, and is not repeated here.
In the embodiment, an excitation signal is applied to a sample to be detected, response data of the sample to be detected based on the excitation signal is determined, and the alcohol content in the sample to be detected is determined according to the response data by combining a pre-trained neural network model; the neural network model learns the corresponding relation between each response data and the alcohol content, so that the alcohol content detection accuracy can be effectively improved, and the alcohol content detection effect is improved.
Fig. 10 is a schematic structural diagram of a detection apparatus according to an embodiment of the present invention.
Referring to fig. 10, the detection apparatus 100 includes:
the alcohol content detecting apparatus 800 in the embodiment shown in fig. 8 is described above.
It should be noted that the explanation of the embodiment of the alcohol content detection method in the foregoing embodiments of fig. 1 to fig. 7 also applies to the detection apparatus 100 of this embodiment, and the implementation principle is similar, and is not repeated here.
In the embodiment, an excitation signal is applied to a sample to be detected, response data of the sample to be detected based on the excitation signal is determined, and the alcohol content in the sample to be detected is determined according to the response data by combining a pre-trained neural network model; the neural network model learns the corresponding relation between each response data and the alcohol content, so that the alcohol content detection accuracy can be effectively improved, and the alcohol content detection effect is improved.
It should be noted that the terms "first," "second," and the like in the description of the present invention are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. In addition, in the description of the present invention, "a plurality" means two or more unless otherwise specified.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (13)
1. An alcohol content detection method is characterized by comprising the following steps:
applying an excitation signal to a sample to be tested;
determining response data of the sample to be tested based on the excitation signal;
determining the alcohol content in the sample to be tested by combining a pre-trained neural network model according to the response data; the neural network model has learned the correspondence between each response data and the alcohol content.
2. The method of claim 1, wherein the neural network model is trained using initial samples with respective response data until the neural network model training is completed when the alcohol content of the initial samples identified by the neural network model matches a pre-labeled alcohol content.
3. The alcohol content testing method according to claim 1 or 2, wherein said applying an excitation signal to a sample to be tested comprises:
and applying a variable-frequency excitation signal to the sample to be tested by adopting a single electrode.
4. The method according to claim 3, wherein the response data is impedance values of the sample to be tested under excitation signals of different frequencies.
5. The alcohol content detecting method according to claim 3, wherein,
removing carbon dioxide in the initial sample before training the neural network model;
when the neural network model is trained, gradually adding a set amount of alcohol to the initial sample from which the carbon dioxide is removed to obtain a plurality of initial samples with different alcohol contents, and taking the obtained plurality of different alcohol contents as the alcohol contents marked by the corresponding initial samples;
and performing iterative training on the neural network model by adopting a plurality of initial samples with labeled alcohol content.
6. The method of claim 3, wherein the single electrode is an interdigitated electrode.
7. An alcohol content detecting device, comprising:
the signal transmitting module is used for applying an excitation signal to a sample to be detected;
a first determination module for determining response data of the sample to be tested based on the excitation signal;
the second determination module is used for determining the alcohol content in the sample to be tested by combining a pre-trained neural network model according to the response data; the neural network model has learned the correspondence between each response data and the alcohol content.
8. The alcohol content detecting device according to claim 7, wherein the neural network model is trained using initial samples having respective response data until the neural network model training is completed when the alcohol content of the initial samples identified by the neural network model matches a pre-labeled alcohol content.
9. The alcohol content detection device according to claim 7 or 8, wherein the signal transmission module is specifically configured to:
and applying a variable-frequency excitation signal to the sample to be tested by adopting a single electrode.
10. The alcohol content detecting apparatus according to claim 9, wherein the response data is impedance values of the sample to be measured at excitation signals of different frequencies.
11. The alcohol content detecting device according to claim 9, further comprising:
the processing module is used for removing carbon dioxide in the initial sample before the neural network model is trained, gradually adding a set amount of alcohol to the initial sample after the carbon dioxide is removed when the neural network model is trained to obtain a plurality of initial samples with different alcohol contents, and taking the obtained plurality of different alcohol contents as the alcohol contents marked by the corresponding initial samples;
and the training module is used for carrying out iterative training on the neural network model by adopting a plurality of initial samples with labeled alcohol content.
12. The alcohol content detecting device according to claim 9, wherein the single electrode is an interdigital electrode.
13. A detection apparatus, comprising:
the alcohol content detecting device according to any one of claims 7 to 12.
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Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1381721A (en) * | 2002-06-04 | 2002-11-27 | 复旦大学 | Portable intelligent electronic nose and its preparing process |
| CN2540633Y (en) * | 2002-06-04 | 2003-03-19 | 复旦大学 | Structure of portable intelligence electronic noise |
| CN1609605A (en) * | 2003-10-21 | 2005-04-27 | 中国科学院电子学研究所 | Sensor array signal processing method for liquor smell detection |
| CN101718734A (en) * | 2009-12-18 | 2010-06-02 | 昆明理工大学 | Method for discriminatnig and processing abnormal sampling data of methane sensor for coal mine |
| US20120291530A1 (en) * | 2010-01-14 | 2012-11-22 | Toyota Jidosha Kabushiki Kaisha | Concentration detecting apparatus |
| US20140091811A1 (en) * | 2012-09-28 | 2014-04-03 | General Electric Company | Systems and methods for monitoring sensors |
| US20150346131A1 (en) * | 2014-06-02 | 2015-12-03 | Case Western Reserve University | Sensor apparatus, systems and methods of making same |
| CN106841325A (en) * | 2017-01-18 | 2017-06-13 | 西安交通大学 | One kind is based on semiconductor gas sensor array detection exhaled gas device |
| CN107132315A (en) * | 2017-05-12 | 2017-09-05 | 盐城工学院 | Signal recognition method, device and volatile organic matter detection device |
| US20170269048A1 (en) * | 2016-03-21 | 2017-09-21 | Virginia Commonwealth University | Portable alcohol tester |
| CN107238638A (en) * | 2017-06-28 | 2017-10-10 | 四川理工学院 | The assay method contacted based on each composition physical and chemical index of Daqu and liquor output and vinosity |
| CN109322655A (en) * | 2018-09-05 | 2019-02-12 | 深圳市联恒星科技有限公司 | A kind of microwave hydro rate detection device and method based on neural network Yu double frequency difference model |
| CN109540978A (en) * | 2018-12-13 | 2019-03-29 | 清华大学 | Odor identification equipment |
-
2020
- 2020-01-07 CN CN202010014394.1A patent/CN111157584A/en not_active Withdrawn
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1381721A (en) * | 2002-06-04 | 2002-11-27 | 复旦大学 | Portable intelligent electronic nose and its preparing process |
| CN2540633Y (en) * | 2002-06-04 | 2003-03-19 | 复旦大学 | Structure of portable intelligence electronic noise |
| CN1609605A (en) * | 2003-10-21 | 2005-04-27 | 中国科学院电子学研究所 | Sensor array signal processing method for liquor smell detection |
| CN101718734A (en) * | 2009-12-18 | 2010-06-02 | 昆明理工大学 | Method for discriminatnig and processing abnormal sampling data of methane sensor for coal mine |
| US20120291530A1 (en) * | 2010-01-14 | 2012-11-22 | Toyota Jidosha Kabushiki Kaisha | Concentration detecting apparatus |
| US20140091811A1 (en) * | 2012-09-28 | 2014-04-03 | General Electric Company | Systems and methods for monitoring sensors |
| US20150346131A1 (en) * | 2014-06-02 | 2015-12-03 | Case Western Reserve University | Sensor apparatus, systems and methods of making same |
| US20170269048A1 (en) * | 2016-03-21 | 2017-09-21 | Virginia Commonwealth University | Portable alcohol tester |
| CN106841325A (en) * | 2017-01-18 | 2017-06-13 | 西安交通大学 | One kind is based on semiconductor gas sensor array detection exhaled gas device |
| CN107132315A (en) * | 2017-05-12 | 2017-09-05 | 盐城工学院 | Signal recognition method, device and volatile organic matter detection device |
| CN107238638A (en) * | 2017-06-28 | 2017-10-10 | 四川理工学院 | The assay method contacted based on each composition physical and chemical index of Daqu and liquor output and vinosity |
| CN109322655A (en) * | 2018-09-05 | 2019-02-12 | 深圳市联恒星科技有限公司 | A kind of microwave hydro rate detection device and method based on neural network Yu double frequency difference model |
| CN109540978A (en) * | 2018-12-13 | 2019-03-29 | 清华大学 | Odor identification equipment |
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Application publication date: 20200515 |