CN104034847A - Accurate smell fingerprint detection method based on the rapid discrete frequency domain analysis theory - Google Patents
Accurate smell fingerprint detection method based on the rapid discrete frequency domain analysis theory Download PDFInfo
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Abstract
An accurate smell fingerprint detection method based on the rapid discrete frequency domain analysis theory is disclosed. The method includes four steps as follows: a step of sampling processing of collected gas data, a step of performing truncation processing, a step of performing signal periodic extension processing, and a step of performing discrete frequency domain transformation. Advantages and beneficial effects of the method are that: the method can effectively eliminate interfering elements in an observation data set and largely increase the operation efficiency, and the method can improve the accuracy of detection through frequency domain and time domain analysis of the interfering elements and through elimination of the interfering elements. Therefore, the method enhances the real-time property of system detection, detection results are more reliable and the detection accuracy is largely improved. The method is suitable for fixed or handheld electronic noses and can largely improve the detection speed, the accuracy, and other technical indexes.
Description
Technical field
The invention belongs to monitored gas environment technical field, particularly relate to a kind of accurate smell fingerprint detection method based on fast discrete frequency-domain analysis theory.
Background technology
In recent years, China's haze weather is more and more serious, and the number of times of annual haze also constantly increases, and has had a strong impact on people's life and health.And the main cause that causes haze is exactly the vehicle exhaust of industrial gaseous waste as the waste gas of the eliminatings such as chemical plant, steel-making and oil refining and in living etc.; for due to these other the feature such as heterogeneity and unevenness; make people face a very large difficult problem to the monitoring of harmful gas, and the information such as real-time and accuracy of monitoring successfully manage and control crisis and all have very important left and right to people.
Occurred in recent years a kind of new smell fingerprint detection method in Electronic Nose market, this system mainly utilizes the smell sensors array of device interior and gas molecular data processing and identification device to detect gas finger-print.In whole system, the effect of Electronic Nose is equivalent to people's olfactory organ, convert after treatment other molecular signals that receive to data point in gas data space, then, foundation is to harmful gas scientific research data setting dependent thresholds, the gas data of accepting is classified, the class of harmful gas is fed back to system front end, trigger relevant warning.
The chemical composition data of the gas molecule that gas detecting system can receive sensor array arranges, to the effective sorting technique of gas data information utilization after arranging, harmful gas is classified with safe gas, then, sorted result is calculated to concentration or the ratio of harmful gas in current scene, according to this feature identification harmful gas.Owing to there being a lot of interference in atmosphere, therefore, the key that specific dusty gas molecule is surveyed, is just how from these gas molecules, to survey and to pollute molecule quickly and accurately, this has just proposed very high technical requirement to the Processing Algorithm of smell sensor.
For existing traditional harmful gas checkout equipment, due to generally not high reason of deal with data amount macrooperation efficiency, make the real-time poor effect detecting in actual monitoring.In addition, due to the limitation of traditional Processing Algorithm, make the detection accuracy of harmful gas poor, so existing monitoring equipment, because its real-time and accuracy are poor, also cannot meet actual monitoring needs far away.
Summary of the invention
The present invention is in order to solve exist in above-mentioned existing detection method consuming time long, can not accurately detect the technical barriers such as harmful gas, a kind of accurate smell fingerprint detection method based on fast discrete frequency-domain analysis theory is provided, the method is applied in Electronic Nose, the concentrated disturbing molecule of observation data can effectively be proposed, and can greatly improve operation efficiency, effectively improve the requirement of real-time and the accuracy of current smell fingerprint detection.
The present invention for addressing this problem taked technical scheme is:
Accurate smell fingerprint detection method based on fast discrete frequency-domain analysis theory of the present invention, accurate smell fingerprint detection method based on fast discrete frequency-domain analysis theory of the present invention, main treatment scheme comprises the sample process, truncation, signal period continuation processing, four steps of discrete frequency domain conversion that gather gas data.Concrete methods of realizing is as follows:
The first step, the sample process of gas data
The signal array being gathered by smell sensor array is carried out to sampling processing.Its sample frequency is made as
, sampling interval is
, wherein,
.
Second step, signal cutout processing
The signal gathering is carried out after sampling processing, then carries out signal cutout processing, suppose that truncated signal is:
Signal cutout is arrived interval
after signal indication form be:
The 3rd step, signal period continuation processing
By the olfactory signal data translation through over-sampling and truncation
form cyclical signal, can by with
carry out convolution and realize this process.Therefore the mathematical expression through periodic extension signal can be expressed as:
Wherein: time-domain signal
The 4th step: discrete frequency domain conversion
If the smell data that collect
for the finite sequence that length is N, can define so
n point discrete frequency domain be transformed to:
And its inverse transformation can be expressed as:
。
Advantage and good effect that the present invention has are:
Accurate smell fingerprint detection method based on the conversion of fast discrete frequency domain of the present invention, the concentrated disturbing molecule of observation data can effectively be proposed, and can greatly improve operation efficiency, and by the frequency-domain and time-domain analysis of disturbing molecule is rejected to disturbing molecule, improve the accuracy detecting.Therefore, the present invention has not only strengthened the real-time that system detects, and testing result is more reliable, greatly improves the accuracy detecting.The present invention is applicable to fixing or hand-hold electric nasus, can greatly improves the technical indicator such as detection speed and accuracy.
Brief description of the drawings
Fig. 1 is the decomposable process flow graph based on the conversion of fast discrete frequency domain of the present invention.
Embodiment
Referring to drawings and Examples, the accurate smell fingerprint detection method based on fast discrete frequency-domain analysis theory of the present invention is described in detail.
Accurate smell fingerprint detection method based on fast discrete frequency-domain analysis theory of the present invention, embodiment is as follows:
The first step, the sample process of olfactory signal
The signal array being gathered by smell sensor array is carried out to sampling processing.Its sample frequency is made as
, sampling interval is
, wherein,
.
Second step, signal cutout processing
The signal gathering is carried out after sampling processing, then carries out signal cutout processing, suppose that truncated signal is:
Signal cutout is arrived interval
after signal indication form be:
The 3rd step, signal period continuation processing
By the olfactory signal data translation through over-sampling and truncation
form cyclical signal, can by with
carry out convolution and realize this process.Therefore the mathematical expression through periodic extension signal can be expressed as:
The 4th step: discrete frequency domain conversion
If the smell data that collect
for the finite sequence that length is N, can define so
n point discrete frequency domain be transformed to:
And its inverse transformation can be expressed as:
。
For fast discrete frequency domain mapping algorithm, important technological difficulties are implementation procedures of this algorithm, and main employing is fast discrete frequency-domain transform method in the present invention, and the circular of this method is so:
it is as follows that frequency domain converts concrete computation process:
Therefore:
Then, the operation result of above formula can be represented by the form of signal flow diagram, calculating process as shown in Figure 1.The useful result of applying this decomposition algorithm is generally to improve operation efficiency, and can more comprehensively analyze collection signal, improves the accuracy to image data classification, and then improves the detection accuracy of system.
Computation process operand divides
for
's
the conversion of some discrete frequency domain, needs after decomposition
individual computing level and
the conversion of some discrete frequency domain, each computing level need to once be answered to take advantage of and is added with computing with twice, and each
the discrete frequency domain conversion of point needs altogether
inferiorly take advantage of again and time be added with computing, after decomposing, total computing comprises altogether
inferior take advantage of again and
be added with.
So the operand after decomposing has reduced about half, then, can also further decompose the sequence after decomposing.By each
the subsequence of point is being two by Parity-decomposition
the subsequence of point obtains:
According to above decomposable process, if
time, the operation times that this algorithm needs is altogether:
Take advantage of again:
Be added with:
And the calculated amount of directly carrying out discrete frequency domain conversion is:
Take advantage of again:
Be added with:
.
By above to computing quantitative analysis, can find out in the time that the data to a large amount of are carried out frequency domain conversion, adopt quick computing more can save time and space than Direct Transform, the useful result being applied in physical device is: the operand that can generally reduce system, greatly improve operation efficiency, strengthen the real-time of equipment.In addition, by can more comprehensively analyzing the signal of frequency domain after conversion, the classification of being more convenient for to collection signal, can more effectively reject the impact of undesired signal on testing result, the accuracy that the system that ensured detects.
Claims (2)
1. the accurate smell fingerprint detection method based on fast discrete frequency-domain analysis theory, is characterized in that, the method comprises the sample process, truncation, signal period continuation processing, four steps of discrete frequency domain conversion that gather gas data; Concrete methods of realizing is as follows:
The first step, the sample process of gas data
The signal array being gathered by smell sensor array is carried out to sampling processing; Sample frequency is made as
, sampling interval is
, wherein,
;
Second step, signal cutout processing
Signal to collection carries out after sampling processing, then carries out signal cutout processing, establishes truncated signal and is:
Signal cutout is arrived interval
after signal indication form be:
The 3rd step, signal period continuation processing
By the olfactory signal data translation through over-sampling and truncation
form cyclical signal, by with
carry out convolution and realize this process; Therefore the mathematical expression through periodic extension signal is expressed as:
Wherein: time-domain signal
The 4th step: discrete frequency domain conversion
If the smell data that collect
for the length finite sequence that is N,
n point discrete frequency domain be transformed to:
And its inverse transformation can be expressed as:
。
2. the accurate smell fingerprint detection method based on fast discrete frequency-domain analysis theory according to claim 1, is characterized in that: the detailed process of discrete frequency domain conversion is as follows:
Therefore:
?
for
's
the conversion of some discrete frequency domain, needs after decomposition
individual computing level and
the conversion of some discrete frequency domain, each computing level need to once be answered to take advantage of and is added with computing with twice, and each
the discrete frequency domain conversion of point needs altogether
inferior take advantage of again and
inferiorly be added with computing,
After decomposing, total computing comprises altogether
inferior take advantage of again and
inferior being added with;
Then, the sequence after decomposing is further decomposed, by each
the subsequence of point is being two by Parity-decomposition
the subsequence of point obtains:
。
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101840001A (en) * | 2010-02-10 | 2010-09-22 | 中国科学院地质与地球物理研究所 | Acquiring method and device of geological structure three-dimensional imaging data |
| CN102594373A (en) * | 2011-01-07 | 2012-07-18 | 北京中科国技信息系统有限公司 | Method for generating low-complexity SSB (Single Side Band) signals of RFID (Radio Frequency Identification Device) system |
| US20120314538A1 (en) * | 2011-06-08 | 2012-12-13 | Chevron U.S.A. Inc. | System and method for seismic data inversion |
| CN103293553A (en) * | 2013-04-17 | 2013-09-11 | 中国海洋石油总公司 | Continuation and correction method for boundary element of earthquake data collected through upper cables and lower cables in complex seabed |
| CN103440871A (en) * | 2013-08-21 | 2013-12-11 | 大连理工大学 | Method for suppressing transient noise in voice |
-
2013
- 2013-12-30 CN CN201310742360.4A patent/CN104034847B/en not_active Expired - Fee Related
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101840001A (en) * | 2010-02-10 | 2010-09-22 | 中国科学院地质与地球物理研究所 | Acquiring method and device of geological structure three-dimensional imaging data |
| CN102594373A (en) * | 2011-01-07 | 2012-07-18 | 北京中科国技信息系统有限公司 | Method for generating low-complexity SSB (Single Side Band) signals of RFID (Radio Frequency Identification Device) system |
| US20120314538A1 (en) * | 2011-06-08 | 2012-12-13 | Chevron U.S.A. Inc. | System and method for seismic data inversion |
| CN103293553A (en) * | 2013-04-17 | 2013-09-11 | 中国海洋石油总公司 | Continuation and correction method for boundary element of earthquake data collected through upper cables and lower cables in complex seabed |
| CN103440871A (en) * | 2013-08-21 | 2013-12-11 | 大连理工大学 | Method for suppressing transient noise in voice |
Non-Patent Citations (3)
| Title |
|---|
| 吴 剑 等: "用频域方法实时计算运动中的脉搏数", 《清华大学学报 (自然科学版 )》 * |
| 潘 楠 等: "基于频域盲解卷积的机械设备状态监测与故障诊断", 《振 动 与 冲 击》 * |
| 章学铜 等: "基于时域和频域信号综合分析的金属探伤研究", 《机电工程》 * |
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