CN104089592A - Pine wood texture detection method - Google Patents
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- CN104089592A CN104089592A CN201310441525.4A CN201310441525A CN104089592A CN 104089592 A CN104089592 A CN 104089592A CN 201310441525 A CN201310441525 A CN 201310441525A CN 104089592 A CN104089592 A CN 104089592A
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Abstract
The invention discloses a pine wood texture detection method. According to the invention, a stress wave is used to detect whether a hole is in pine wood; if the hole is in the pine wood, an acoustic surface wave is used to further detect the position of the hole; computer software is used to establish a three dimensional model for the pine wood and the hole; and the hole in the pine wood is accurately showed. The pine wood texture detection method provided by the invention has the advantages that the size of the hole in the pine wood can be accurately detected; the position of the hole in the pine wood can be determined; the three dimensional models of the pine wood and the hole in the pine wood can be acquired; and the pine wood texture detection method has the characteristics of fast detection and good economy.
Description
Technical field
The present invention relates to a kind of lumber quality detection method, especially relate to a kind of position and big or small pine quality detection method that can accurately detect the hole in pine.
Background technology
Dynamic Non-Destruction Measurement is development in recent years timber quality detection method rapidly, can not destroy under the prerequisite of the shape of timber own, structure, position Fast Measurement timberphysics mechanical property.Stress wave timber Dynamic Non-Destruction Measurement can not destroy under the prerequisite of timber usability, detects rapidly size, specification and the basic physical property etc. of timber, and based on this advantage, stress wave Dynamic Non-Destruction Measurement more and more came into one's own in recent years.
Stress wave can be for detection of the quality of the thick timber of diameter or large tree, but there are some shortcomings in stress wave, as the method cannot accurately judge the size of hole and the relative position in trunk, a lot of timber or the tree that lives, owing to damaging by worms etc., reason forms inner hollow structure.If the application of such timber under construction, along with the prolongation of time, inner void can rot to cause construction stress structure to change gradually, causes building collapse accident so, brings threat to people's personal safety.
Chinese patent mandate publication number: CN102706963A, authorize open day on October 3rd, 2012, the inner rotten stress wave lossless detection device of a kind of ancient tree and historic building structure is disclosed, this device comprises a plurality of sensors, sensor fastening device, power hammer, micro-damage type pin type connector, data handling system and the inner rotten fault imaging software of timber, and described sensor, described data handling system and the computing machine that described imaging software is installed are connected by some wires.This invention exists cannot accurately determine the position of hole and the big or small deficiency of hole.
Summary of the invention
Goal of the invention of the present invention is cannot accurately determine the position of hole and the big or small deficiency of hole in order to overcome prospection stress wave detection method of the prior art, provides a kind of and can accurately detect the position of the hole in pine and the pine quality detection method of size.
To achieve these goals, the present invention is by the following technical solutions:
A pine quality detection method, a kind of pine pick-up unit detecting for pine quality comprises acoustic surface wave device, distance measuring sensor and several shockwave sensors; On shockwave sensor, distance measuring sensor and acoustic surface wave device, be respectively equipped with the data-interface for being electrically connected to computing machine; It is characterized in that, described acoustic surface wave device comprises counter, oscillator and SAW (Surface Acoustic Wave) resonator; Oscillator and SAW (Surface Acoustic Wave) resonator form oscillation circuit, and counter is electrically connected to oscillation circuit, and counter is provided with the data-interface for being electrically connected to computing machine, and SAW (Surface Acoustic Wave) resonator is provided with two electrodes, and distance measuring sensor is located on an electrode; Described detection method comprises the steps:
(1-1) in computing machine, set in advance stochastic resonance system model and hole wall thickness forecast model;
In computing machine, set
n span and the absolute value A of standard signal to noise ratio (S/N ratio) valley
icorresponding relation, i=1, L, n; Specification error threshold value d, sets surface acoustic wave detection threshold S; Wherein,
the diameter of hole,
it is the pine diameter of hole position;
(1-2) two ends of pine are made as respectively to initiating terminal and end, pulse hammer and the circumferencial direction of each shockwave sensor along pine are fixed on the outer peripheral face of initiating terminal;
(1-3) pulse hammer is knocked pine outer peripheral face, the inner stress wave that produces of pine, and the stress wave signal that each shockwave sensor is detected is input in computing machine, and computing machine calculates the mean value of each stress wave signal, obtains stress wave average signal;
(1-4) in stress wave average signal, extract 1 complete stress wave pulse signal, the described stress wave pulse signal I (t) in 0 to 100ms is inputted in stochastic resonance system model, stochastic resonance system model is resonated; Computing machine utilizes snr computation formula to calculate output signal-to-noise ratio SNR;
Accidental resonance technology is shown up prominently in detection data feature values extraction field at present.This theory is proposed in 1981 by Italian physicist Benzi, in order to the phenomenon of explaining that the earth meteorological glacial epoch in time immemorial and cycle warm climate phase alternately occur.Accidental resonance has three elements: nonlinear system, weak signal and noise source.From signal process angle, consider, accidental resonance is in nonlinear properties transmitting procedure, by regulating intensity or other parameter of system of noise, make system output reach optimum value, in fact also can think the collaborative state of input signal, nonlinear system, noise.
Generally, input the signal that external force can be thought desirable electric nasus system in flip-flop-model, noise is the interchannel noise of introducing in testing process, and the input of bistable system (signal plus noise) is as the detection signal of electric nasus system reality.Under the excitation of excitation noise, system produces accidental resonance, and now output signal is greater than input signal, thereby has played the effect that signal amplifies.Meanwhile, accidental resonance is transformed into the noise energy in part detection signal in signal and goes, thereby has effectively suppressed the noisiness in detection signal.Therefore, stochastic resonance system is equivalent to improve the effect of output signal-noise ratio, and signal, excitation noise and bistable system can be regarded an efficient signal processor as.On above technical foundation, stochastic resonance system output signal-to-noise ratio analytical technology can be reacted the essential characteristic information of sample preferably.
What accidental resonance output signal-to-noise ratio characteristic information reflected is the essential information of sample, and this characteristic information does not change with the restriction of detection method or multiplicity, only relevant with the character of sample, is conducive to the demarcation of properties of samples, improves accuracy of detection.
Accidental resonance analytical approach favorable reproducibility, repeats 100 times and calculates, and the resultant error ratio of output is no more than 0.1%.And the error ratio of the frequency signal error rate that simple stress wave detects after than accidental resonance Analysis signal-to-noise ratio (SNR) exceeds several times.
(1-5) computing machine draws the output signal-to-noise ratio curve of stochastic resonance system model, obtains the signal to noise ratio (S/N ratio) valley of signal to noise ratio (S/N ratio) curve, and using the absolute value of signal to noise ratio (S/N ratio) valley as signal to noise ratio (S/N ratio) eigenwert F;
(1-6) when
to make the hole in pine be A to computing machine
icorresponding
the judgement of span;
(1-7) when
and measuring position is during apart from end >1 centimetre of pine, pulse hammer and each shockwave sensor are taken off from the outer peripheral face of pine, pulse hammer and each shockwave sensor are moved to pine end gradually, and pulse hammer and each shockwave sensor are fixed on the pine outer peripheral face apart from 0.5 to 1 centimetre of measuring position last time successively, return to step (1-3);
When
and measuring position is during apart from end≤1 of pine centimetre, and pulse hammer quits work, and computing machine is made the judgement of pine fine texture, and will judge that information shows on computer screen;
When
proceed to step (1-8);
(1-8) measure the wall thickness of hole:
Step a, takes off pulse hammer and each shockwave sensor from the outer peripheral face of pine;
Two electrodes of SAW (Surface Acoustic Wave) resonator are fixed on the outer peripheral face of initiating terminal of pine to 2 to 3 millimeters along the circumferencial direction interval of pine, two electrodes; The outer peripheral face of the end of pine is provided with for reflecting the ring-type back-up ring of the measuring-signal of distance measuring sensor;
Step b, SAW (Surface Acoustic Wave) resonator work, counter detects surface acoustic wave response frequency Freq
sAW, surface acoustic wave response frequency Freq
sAWbe input in computing machine, computing machine utilizes hole wall thickness forecast model to calculate hole in pine with respect to the wall thickness of electrode side;
Step c, moves two electrodes of SAW (Surface Acoustic Wave) resonator and the fixing position of pine outer peripheral face along the circumferencial direction of pine, and repeating step b detects, and obtains several wall thickness d;
Steps d, the distance value between the electrode that distance measuring sensor is detected and pine end and being stored in computing machine with corresponding each wall thickness d of distance value;
Step e, repeating step a to d carries out the detection of axial scan formula to pine;
(1-9) in advance the outer peripheral face of pine is measured, the coordinate of the point on the outer peripheral face of setting pine, according to the distance value between the coordinate of the point of the outer peripheral face of pine, electrode and pine end and each wall thickness d corresponding with distance value, computing machine is set up the three-dimensional model of pine and pine Hole.
Stress wave has the advantages that penetration capacity is strong, and the present invention adopts stress wave first to detect and in the pine that diameter is large, whether has hole;
While having hole, with surface acoustic wave, further detect the position of hole, and use computer software to set up three-dimensional model for pine and hole, thereby the hole in pine is accurately presented.
Simple stress wave can only detect in trees, whether there is hole, the detection method that adopts stress wave to combine with surface acoustic wave in the present invention, stress wave is mainly realized the size detection of pine inner void, and surface acoustic wave Detection Techniques can precise positioning hole position, the two is in conjunction with the accurate judgement that can realize pine Hole size and position simultaneously, this is significant for the judgement pine inside situation of damaging by worms, and can effectively improve building safety, reduces economic loss.
As preferably, in described step b, hole wall thickness forecast model is
As preferably, described stochastic resonance system model is
Wherein, V (x) is non-linear symmetric potential function, and ξ (t) is white Gaussian noise, and A is input signal strength, and D is noise intensity, and t is the Brownian movement Particles Moving time, and x is the coordinate of Particles Moving.
As preferably, described snr computation formula is
Wherein, ω is signal frequency, and Ω is angular frequency, and S (ω) is signal spectral density, S
n(Ω) be the noise intensity in signal frequency range.
As preferably, described surface acoustic wave detection threshold S is 1% to 2.5%.
As preferably, described error threshold d is 0.1 to 0.5.
As preferably, the centre frequency of SAW (Surface Acoustic Wave) resonator is 433.92MHZ.
Therefore, the present invention has following beneficial effect: (1) can accurately detect the size of the hole in pine; (2) can determine the position of pine Hole; (3) can access the three-dimensional model of the hole in pine and pine; (4) detect quick, good economy performance.
Accompanying drawing explanation
Fig. 1 is a kind of process flow diagram of the present invention;
Fig. 2 is theory diagram of the present invention;
Fig. 3 is stress wave pulse signal figure of the present invention;
Fig. 4 is the matched curve between surface acoustic wave response frequency of the present invention and wall thickness;
Fig. 5 is the output signal-to-noise ratio curve of stress wave detection signal of the present invention;
Fig. 6 is pine cross-sectional view of the present invention and coordinate system figure;
Fig. 7 is a kind of structural representation of ring-type back-up ring of the present invention.
In figure: acoustic surface wave device 1, distance measuring sensor 2, shockwave sensor 3, counter 4, oscillator 5, SAW (Surface Acoustic Wave) resonator 6, electrode 7, computing machine 8, pine 9, hole 10, ring-type back-up ring 11.
Embodiment
Below in conjunction with the drawings and specific embodiments, the present invention will be further described.
Embodiment is as shown in Figure 2 a kind of pine quality detection method, and the pine pick-up unit detecting for pine quality comprises acoustic surface wave device 1, laser range sensor 2 and 6 shockwave sensors 3; On shockwave sensor, laser range sensor and acoustic surface wave device, be respectively equipped with the data-interface for being electrically connected to computing machine 8;
Acoustic surface wave device comprises counter 4, oscillator 5 and SAW (Surface Acoustic Wave) resonator 6; Oscillator and SAW (Surface Acoustic Wave) resonator form oscillation circuit, and counter is electrically connected to oscillation circuit, and counter is provided with the data-interface for being electrically connected to computing machine, and SAW (Surface Acoustic Wave) resonator is provided with two electrodes 7, and distance measuring sensor is located on an electrode;
In computing machine, set in advance stochastic resonance system model:
Wherein, V (x) is non-linear symmetric potential function, and ξ (t) is white Gaussian noise, and A is input signal strength, and D is noise intensity, and t is the Brownian movement Particles Moving time, and x is the coordinate of Particles Moving;
In computing machine, set hole wall thickness forecast model:
In computing machine, set the diameter of hole
pine diameter with hole position
between ratio
4 spans and the absolute value A of standard signal to noise ratio (S/N ratio) valley
icorresponding relation, i=1, L, 4; Specification error threshold value d=0.2, sets surface acoustic wave detection threshold S=2%;
and A
icorresponding relation be to obtain by experiment: with stress wave to difference
the pine of the hole of span detects, and obtains signal I (t), by frequency signal I (t) input stochastic resonance system model, stochastic resonance system model is resonated; Computing machine draws the output signal-to-noise ratio curve of stochastic resonance system model, obtains the signal to noise ratio (S/N ratio) valley of signal to noise ratio (S/N ratio) curve;
To this hole duplicate detection 100 times, obtain the absolute value of 100 signal to noise ratio (S/N ratio) valleies, the absolute value of signal to noise ratio (S/N ratio) valley is averaged, and measures the size of hole and the pine diameter dimension of detection position of these trees simultaneously, thereby obtain
with A
ibetween corresponding relation.
In the present embodiment, when
be defined as duck eye pine, corresponding A
1=83.7dB;
When
be defined as middle hole pine, corresponding A
2=76.8dB;
When
be defined as large hole pine, corresponding A
3=70.3dB;
When
be defined as rotten hollow of pine, corresponding A
4=63.6dB.
Described detection method as shown in Figure 1 comprises the steps:
Step 100, is made as respectively initiating terminal and end by the two ends of pine, and with fixing belt, by 6 shockwave sensors and pulse hammer, the circumferencial direction along pine is evenly fixed on the outer peripheral face of pine initiating terminal;
Step 200, pulse hammer is knocked pine outer peripheral face, the inner stress wave that produces of pine, the stress wave signal of 6 shockwave sensor detections is input in computing machine, and computing machine calculates the mean value of 6 stress wave signals, obtains stress wave average signal;
Step 300, in stress wave average signal, extracts 1 complete stress wave pulse signal as shown in Figure 3, and the described stress wave pulse signal I (t) in 0 to 100ms is inputted in stochastic resonance system model, and stochastic resonance system model is resonated;
Computing machine utilizes snr computation formula
Calculate output signal-to-noise ratio SNR; Wherein, ω is signal frequency, and Ω is angular frequency, and S (ω) is signal spectral density, S
n(Ω) be the noise intensity in signal frequency range;
Step 400, computing machine draws the output signal-to-noise ratio curve of stochastic resonance system model, obtains the signal to noise ratio (S/N ratio) valley of signal to noise ratio (S/N ratio) curve, and using the absolute value of signal to noise ratio (S/N ratio) valley as signal to noise ratio (S/N ratio) eigenwert F;
In the present embodiment, computing machine draws output signal-to-noise ratio curve as shown in Figure 5, and signal to noise ratio (S/N ratio) valley is-83.8dB that F is 83.8dB;
Step 500, when
to make the hole in pine be A to computing machine
icorresponding
the judgement of span;
In the present embodiment,
So the pine in the present embodiment is duck eye pine.
Step 600, when
and measuring position is during apart from end >1 centimetre of pine, pulse hammer and 6 shockwave sensors are taken off from the outer peripheral face of pine, pulse hammer and 6 shockwave sensors are moved to pine end gradually, and with fixing belt, pulse hammer and each shockwave sensor are fixed on the pine outer peripheral face apart from 0.5 centimetre of measuring position last time successively, return to step 300;
When
and measuring position is during apart from end≤1 of pine centimetre, and pulse hammer stops knocking, and computing machine is made the judgement of pine fine texture, and will judge that information shows on computer screen;
When
proceed to step 700;
Step 700, the wall thickness of measurement hole:
Step 701, takes off pulse hammer and 6 shockwave sensors from the outer peripheral face of pine;
With fixing belt, two electrodes of SAW (Surface Acoustic Wave) resonator are fixed on the outer peripheral face of pine initiating terminal, two electrodes along the circumferencial direction of pine at a distance of 2 millimeters; The outer peripheral face of the end of pine be provided with as shown in Figure 7 for reflecting the ring-type back-up ring 11 of the measuring-signal of distance measuring sensor;
Step 702, SAW (Surface Acoustic Wave) resonator work, counter detects surface acoustic wave response frequency Freq
sAW, surface acoustic wave response frequency Freq
sAWbe input in computing machine, computing machine utilizes hole wall thickness forecast model
calculate hole in pine with respect to the wall thickness of electrode side;
Hole wall thickness forecast model is to detect through the different holes to pine, thereby obtains 12 wall thickness and the Freq corresponding with this wall thickness
sAW, by 12 wall thickness and the Freq corresponding with this wall thickness
sAWform point, 12 points are carried out to linear fit, obtain matched curve as shown in Figure 4, thereby reach hole wall thickness forecast model.
Step 703, moves two electrodes of SAW (Surface Acoustic Wave) resonator and the fixing position of pine outer peripheral face along the circumferencial direction of pine, and the fixed position of electrode is respectively pine outer peripheral face right side, left side, front side and rear side; Repeating step 702 detects, and obtains 4 wall thickness d1, d2, d3, d4;
Step 704, electrode and the distance value between pine end and 4 wall thickness d1 that distance measuring sensor is detected, d2, d3, d4 is stored in computing machine;
Step 705, by two electrodes of SAW (Surface Acoustic Wave) resonator, initiating terminal to the end by pine moves gradually, and repeating step 701 to 704 detects;
Step 800, pine cross-sectional view and coordinate system as shown in Figure 6, in advance the outer peripheral face of pine 9 is measured, obtain the coordinate of the point on the outer peripheral face of pine, according to distance value and the d1 between the coordinate of the point of the outer peripheral face of pine, electrode and pine end, d2, d3, d4, computing machine is set up the three-dimensional model of pine and pine Hole 10.
In the present embodiment, suppose that timber hollow space is a circle, according to the value of d1, d2, d3 and d4, the coordinate that computing machine calculates four points that obtain hole is: d1 (x1, y1), d2 (x2, y2), d3 (x3, y3), d4 (x4, y4), presetting round central coordinate of circle is (x0, y0);
First make the perpendicular bisector of d1 and d3 line segment:
In like manner, make the perpendicular bisector of d2 and d4 line segment:
Y
s1and y
s2intersection point be required central coordinate of circle (x0, y0), (x0, y0) to d1, d2, d3, the distance of d4 equates, makes and take (x0, y0) as the center of circle, circumference is through putting a d1, d2, d3, the circle of d4.Make respectively the disalignment of electrode to the circle of measuring position, and in conjunction with the distance of described distance of round pine end, thereby in computing machine, make the three-dimensional model of pine and pine Hole.
Also can d1, d2, d3 and d4 be coupled together with smooth curve, form the hole sectional view of the pine xsect of current detection, make respectively the disalignment of electrode to the hole sectional view of measuring position, and in conjunction with hole sectional view the distance apart from pine end, in computing machine, make the three-dimensional model of pine and pine Hole.
Should be understood that the present embodiment is only not used in and limits the scope of the invention for the present invention is described.In addition should be understood that those skilled in the art can make various changes or modifications the present invention after having read the content of the present invention's instruction, these equivalent form of values fall within the application's appended claims limited range equally.
Claims (7)
1. a pine quality detection method, a kind of pine pick-up unit detecting for pine quality comprises acoustic surface wave device (1), distance measuring sensor (2) and several shockwave sensors (3); On shockwave sensor, distance measuring sensor and acoustic surface wave device, be respectively equipped with the data-interface for being electrically connected to computing machine; It is characterized in that, described acoustic surface wave device comprises counter (4), oscillator (5) and SAW (Surface Acoustic Wave) resonator (6); Oscillator and SAW (Surface Acoustic Wave) resonator form oscillation circuit, counter is electrically connected to oscillation circuit, counter is provided with the data-interface for being electrically connected to computing machine (8), and SAW (Surface Acoustic Wave) resonator is provided with two electrodes (7), and distance measuring sensor is located on an electrode; Described detection method comprises the steps:
(1-1) in computing machine, set in advance stochastic resonance system model and hole wall thickness forecast model;
In computing machine, set
n span and the absolute value A of standard signal to noise ratio (S/N ratio) valley
icorresponding relation, i=1, L, n; Specification error threshold value d, sets surface acoustic wave detection threshold S; Wherein,
the diameter of hole,
it is the pine diameter of hole position;
(1-2) two ends of pine are made as respectively to initiating terminal and end, pulse hammer and the circumferencial direction of each shockwave sensor along pine are fixed on the outer peripheral face of initiating terminal;
(1-3) pulse hammer is knocked pine outer peripheral face, the inner stress wave that produces of pine, and the stress wave signal that each shockwave sensor is detected is input in computing machine, and computing machine calculates the mean value of each stress wave signal, obtains stress wave average signal;
(1-4) in stress wave average signal, extract 1 complete stress wave pulse signal, the described stress wave pulse signal I (t) in 0 to 100ms is inputted in stochastic resonance system model, stochastic resonance system model is resonated; Computing machine utilizes snr computation formula to calculate output signal-to-noise ratio SNR;
(1-5) computing machine draws the output signal-to-noise ratio curve of stochastic resonance system model, obtains the signal to noise ratio (S/N ratio) valley of signal to noise ratio (S/N ratio) curve, and using the absolute value of signal to noise ratio (S/N ratio) valley as signal to noise ratio (S/N ratio) eigenwert F;
(1-6) when
to make the hole in pine be A to computing machine
icorresponding
the judgement of span;
(1-7) when
and measuring position is during apart from end >1 centimetre of pine, pulse hammer and each shockwave sensor are taken off from the outer peripheral face of pine, pulse hammer and each shockwave sensor are moved to pine end gradually, and pulse hammer and each shockwave sensor are fixed on the pine outer peripheral face apart from 0.5 to 1 centimetre of measuring position last time successively, return to step (1-3);
When
and measuring position is during apart from end≤1 of pine centimetre, and pulse hammer quits work, and computing machine is made the judgement of pine fine texture, and will judge that information shows on computer screen;
When
proceed to step (1-8);
(1-8) measure the wall thickness of hole:
Step a, takes off pulse hammer and each shockwave sensor from the outer peripheral face of pine;
Two electrodes of SAW (Surface Acoustic Wave) resonator are fixed on the outer peripheral face of initiating terminal of pine to 2 to 3 millimeters along the circumferencial direction interval of pine, two electrodes; The outer peripheral face of the end of pine is provided with for reflecting the ring-type back-up ring (11) of the measuring-signal of distance measuring sensor;
Step b, SAW (Surface Acoustic Wave) resonator work, counter detects surface acoustic wave response frequency Freq
sAW, surface acoustic wave response frequency Freq
sAWbe input in computing machine, computing machine utilizes hole wall thickness forecast model to calculate hole in pine with respect to the wall thickness of electrode side;
Step c, moves two electrodes of SAW (Surface Acoustic Wave) resonator and the fixing position of pine outer peripheral face along the circumferencial direction of pine, and repeating step b detects, and obtains several wall thickness d;
Steps d, the distance value between the electrode that distance measuring sensor is detected and pine end and being stored in computing machine with corresponding each wall thickness d of distance value;
Step e, repeating step a to d carries out the detection of axial scan formula to pine;
(1-9) in advance the outer peripheral face of pine is measured, the coordinate of the point on the outer peripheral face of setting pine, according to the distance value between the coordinate of the point of the outer peripheral face of pine, electrode and pine end and each wall thickness d corresponding with distance value, computing machine is set up the three-dimensional model of pine and pine Hole.
2. pine quality detection method according to claim 1, is characterized in that, in described step b, hole wall thickness forecast model is
3. pine quality detection method according to claim 1, is characterized in that, described stochastic resonance system model is
Wherein, V (x) is non-linear symmetric potential function, and ξ (t) is white Gaussian noise, and A is input signal strength, and D is noise intensity, and t is the Brownian movement Particles Moving time, and x is the coordinate of Particles Moving.
4. pine quality detection method according to claim 1, is characterized in that, described snr computation formula is
Wherein, ω is signal frequency, and Ω is angular frequency, and S (ω) is signal spectral density, S
n(Ω) be the noise intensity in signal frequency range.
5. pine quality detection method according to claim 1, is characterized in that, described surface acoustic wave detection threshold S is 1% to 2.5%.
6. according to the pine quality detection method described in claim 1 or 2 or 3 or 4 or 5, it is characterized in that, described error threshold d is 0.1 to 0.5.
7. according to the pine quality detection method described in claim 1 or 2 or 3 or 4 or 5, it is characterized in that, the centre frequency of SAW (Surface Acoustic Wave) resonator is 433.92MHZ.
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