Patent Description:
Structures generate acoustic emissions (AEs) due to development of cracks, friction, or the like inside the structures. AEs are elastic waves that are generated due to development of fatigue cracks of a material. Internal deterioration of a structure can be evaluated by detecting AEs by sensors installed on surfaces of the structure and analyzing signals obtained through the detection. Usually, sensors are adhered to the surfaces of a structure which is to be subjected to degradation evaluation with an adhesive or the like. However, the adhesion of sensors may be insufficient due to defective adhesion work, changes over time, or the like. Such insufficient adhesion may lead to a reduction in the accuracy of deterioration evaluation of the structure or lead to misdiagnosis. Moreover, sensors adhered insufficiently may be at a risk falling off of the surfaces of the structure and thus there is a need to take safety measures.

<NPL> relates to the use of self-diagnostic methods for assessing active sensor integrity.

<NPL> relates to the use of self-diagnostic methods for assessing active sensor integrity and references the above mentioned paper.

<CIT> relates to at least three ultrasonic sensors which are mounted to a plasma treatment device out of which, the first ultrasonic sensor is used for wave transmission, and the second and third ultrasonic sensors are used for wave reception. The ultrasonic signal transmitted from the first ultrasonic sensor is received by the second and third ultrasonic sensors and the contact state of the first, second and third ultrasonic sensors are confirmed by successively switching the function of those sensors from transmission use to receiving use.

An object to be achieved by the present invention is to provide a sensor adhesion state determination system, and a sensor adhesion state determination method which can determine the adhesion states of sensors adhered to a structure.

In a first aspect, a sensor adhesion state determination system is provided as recited in claim <NUM>. In a second aspect, a method is provided as recited in claim <NUM>.

Hereinafter, a sensor adhesion state determination system, and a sensor adhesion state determination method will be described with reference to the drawings.

<FIG> is a diagram showing a system configuration of a sensor adhesion state determination system <NUM> according to an example useful for understanding the invention. The sensor adhesion state determination system <NUM> is used to determine the adhesion state of a sensor adhered to a structure. In the example of <FIG>, a bridge will be described as an example of the structure, but the structure is not necessarily limited to a bridge. For example, the structure may be of any type as long as it is a structure that generates elastic waves due to occurrence or development of cracks or due to an external impact (for example, rain, artificial rain, or the like). Bridges are not limited to structures that are laid over rivers and valleys but also include various structures provided above ground (for example, highway overpasses).

The sensor adhesion state determination system <NUM> includes a plurality of AE sensors <NUM>-<NUM> to <NUM>-n (where n is an integer of <NUM> or more) and a signal processor <NUM>. The AE sensors <NUM>-<NUM> to <NUM>-n and the signal processor <NUM> are connected such that they can communicate with each other by wire or wirelessly. In the following description, the AE sensors <NUM>-<NUM> to <NUM>-n will be referred to as AE sensors <NUM> when they are not distinguished.

The AE sensors <NUM> are adhered to a surface of the structure which is to be subjected to deterioration evaluation with an adhesive or the like. For example, the AE sensors <NUM> are adhered to a concrete floor slab <NUM> of the bridge. Each of the AE sensors <NUM> has an oscillation function to generate elastic waves having a specific frequency and a detection function to detect elastic waves generated from the structure. That is, the AE sensor <NUM> has a combination of an oscillating unit and a detecting unit as a measuring device. The oscillation function is a function of oscillating at a specific frequency to generate pulses of elastic waves at the adhesion portion between the AE sensor <NUM> and the surface of the structure. The elastic waves generated by the oscillation function of the AE sensor <NUM> propagate through the structure.

The AE sensor <NUM> may perform oscillation at a preset time, at intervals of a preset period, or when an instruction is made by a user. The AE sensor <NUM> has a piezoelectric element, detects an elastic wave generated from the structure, and converts the detected elastic wave into a voltage signal (an AE source signal). The AE sensor <NUM> performs processing such as amplification and frequency limiting on the AE source signal and outputs the processed signal to the signal processor <NUM>. An acceleration sensor may be used instead of the AE sensor <NUM>. In this case, the acceleration sensor performs the same processing as that of the AE sensor <NUM> and outputs the processed signal to the signal processor <NUM>.

The signal processor <NUM> receives the AE source signals processed by the AE sensors <NUM> as inputs. The signal processor <NUM> determines the adhesion state of the oscillating AE sensor <NUM> on the basis of frequencies obtained from the input AE source signals. For example, the signal processor <NUM> determines whether the adhesion of the AE sensor <NUM> is in a good state or in a defective state. The signal processor <NUM> functions as a sensor adhesion state determination device. The signal processor <NUM> holds identification information of all AE sensors <NUM> connected to the signal processor <NUM>.

Next, the functional configuration of the signal processor <NUM> will be described.

The signal processor <NUM> includes a central processing unit (CPU), a memory, an auxiliary storage device, or the like connected via buses and executes an adhesion state determination program. By executing the adhesion state determination program, the signal processor <NUM> functions as a device including a calculator <NUM>, a reference information storage <NUM>, and a determiner <NUM>. It is to be noted that all or a part of each of the functions of the signal processor <NUM> may be realized using hardware such as an application specific integrated circuit (ASIC), a programmable logic device (PLD), or a field programmable gate array (FPGA). The adhesion state determination program may be recorded in a computer readable recording medium. The computer readable recording medium is, for example, a storage device such as a flexible disk, a magneto-optical disk, a ROM, a portable medium such as a CD-ROM, or a hard disk installed in a computer system. The adhesion state determination program may also be transmitted and received via an electric communication line.

The calculator <NUM> calculates a peak frequency from the input AE source signal.

The reference information storage <NUM> is configured using a storage device such as a magnetic hard disk device or a semiconductor storage device. The reference information storage <NUM> stores information of a reference range. The reference range indicates a peak frequency range from which it is possible to determine whether or not the adhesion is in a good state. The reference range may be set appropriately. The reference information storage <NUM> may store information of the reference range for each sensor type.

The determiner <NUM> determines the adhesion state of the oscillating AE sensor <NUM> on the basis of both the peak frequency of each AE sensor <NUM> calculated by the calculator <NUM> and the reference range.

<FIG> is a diagram showing an example of data obtained by measurement. The data shown in <FIG> is data regarding all AE sensors <NUM> which is obtained by adhering fifteen AE sensors <NUM> to a surface of a structure and causing the AE sensors <NUM> to sequentially oscillate. The horizontal axis represents the oscillating AE sensors <NUM> and the vertical axis represents peak frequencies. <FIG> shows an example in which pulses of elastic waves are emitted as each of the AE sensors <NUM> sequentially oscillates at regular intervals. A dot sequence shown near a peak frequency of <NUM> represents pulses of elastic waves generated by oscillation of each of the AE sensors <NUM>. A dot sequence shown at peak frequencies <NUM> to <NUM> represents peak frequencies obtained from AE source signals based on elastic waves detected by the other AE sensors <NUM>. In <FIG>, R represents a reference range. Although the AE source signals have peak frequencies of about <NUM>, the peak frequencies vary due to the influence of the positional relationship of the AE sensors <NUM> or the like.

Among the AE sensors <NUM> shown in <FIG>, an AE sensor <NUM> indicated by "<NUM>" and an AE sensor <NUM> indicated by "<NUM>" are in an insufficient adhesion state although they can detect signals. From <FIG>, it can be seen that when an elastic wave is generated by oscillation of an AE sensor <NUM> which is in a defective adhesion state, peak frequencies based on elastic waves as which the other AE sensors <NUM> has detected the generated elastic wave are below a reference range R. Thus, it is possible to determine the state of the oscillating AE sensor <NUM> using peak frequencies of elastic waves as which the other AE sensors <NUM> have detected the elastic wave generated by oscillation of the AE sensor <NUM>. In the following description, it is assumed that the distribution data shown in <FIG> is a frequency distribution.

<FIG> is a sequence diagram showing a process flow for the sensor adhesion state determination system <NUM> of <FIG>. In <FIG>, the case in which there are four AE sensors <NUM> will be described as an example. In <FIG>, the four AE sensors <NUM> will be described as a first sensor, a second sensor, a third sensor, and a fourth sensor.

The first sensor oscillates at a specific frequency (step S101). Vibration is transmitted to the structure due to the oscillation of the first sensor and pulses of elastic waves are generated from the structure due to the vibration. Pulses (elastic waves) generated from the structure propagate through the structure and are detected by the second to fourth sensors. The second sensor converts the detected elastic waves into an AE source signal, performs processing on the AE source signal, and outputs the processed signal to the signal processor <NUM> (step S102). The third sensor converts the detected elastic waves into an AE source signal, performs processing on the AE source signal, and outputs the processed signal to the signal processor <NUM> (step S103). The fourth sensor converts the detected elastic waves into an AE source signal, performs processing on the AE source signal, and outputs the processed signal to the signal processor <NUM> (step S104).

The calculator <NUM> receives the AE source signals output from the sensors as inputs. The calculator <NUM> calculates peak frequencies f2, f3 and f4 from the input AE source signals (step S105). The peak frequency f2 indicates a peak frequency of the AE source signal output from the second sensor. The peak frequency f3 indicates a peak frequency of the AE source signal output from the third sensor. The peak frequency f4 indicates a peak frequency of the AE source signal output from the fourth sensor. The calculator <NUM> outputs the calculated peak frequencies f2, f3, and f4 to the determiner <NUM>. The determiner <NUM> generates a frequency distribution on the basis of the peak frequencies f2, f3 and f4 output from the calculator <NUM> (step S106). That is, the determiner <NUM> generates a frequency distribution with a horizontal axis representing the first sensor and a vertical axis representing the peak frequency. In this case, the determiner <NUM> plots peak frequencies corresponding to the peak frequencies f2, f3 and f4 output from the calculator <NUM> at the positions of the peak frequencies. When a pulse is emitted a plurality of times from the first sensor, the determiner <NUM> performs the same processing the plurality of times. Through this processing, the determiner <NUM> generates a frequency distribution as shown in <FIG>.

Thereafter, the determiner <NUM> determines whether or not the peak frequencies f2, f3, and f4 in the frequency distribution are within the reference range on the basis of both the generated frequency distribution and the information of the reference range stored in the reference information storage <NUM> (step S107). When the peak frequencies f2, f3 and f4 are within the reference range (YES in step S107), the determiner <NUM> determines that the adhesion of the first sensor is good (step S108). That is, the determiner <NUM> determines that the adhesion of first sensor is in a good state.

On the other hand, when the peak frequencies f2, f3 and f4 are not within the reference range (NO in step S107), the determiner <NUM> determines that the adhesion of the first sensor is defective (step S109). That is, the determiner <NUM> determines that the adhesion of the first sensor is in a defective state.

The sensor adhesion state determination system <NUM> performs the processing of <FIG> for each AE sensor <NUM>. For example, when the second sensor emits a pulse, the signal processor <NUM> determines the adhesion state of the second sensor on the basis of elastic waves detected by the first sensor, the third sensor, and the fourth sensor. The method of determining the adhesion state is the same as described above.

According to the sensor adhesion state determination system <NUM> configured as described above, the signal processor <NUM> calculates peak frequencies from AE source signals based on elastic waves detected by the AE sensors <NUM>, determines that the adhesion state of the oscillating AE sensor <NUM> is good when the calculated peak frequencies are within the reference range, and determines that the adhesion state of the oscillating AE sensor <NUM> is defective when the calculated peak frequencies are not within the reference range. Therefore, it is possible to determine the adhesion state of each sensor adhered to the structure.

Modified examples of the sensor adhesion state determination system <NUM> will be described below.

In the example of <FIG> the determiner <NUM> generates the frequency distribution. However, the determiner <NUM> may not generate the frequency distribution. In this case, on the basis of peak frequencies calculated by the calculator <NUM> and a reference range, the determiner <NUM> determines whether or not the peak frequencies are within the reference range.

When a specific number of (for example, two or three) peak frequencies among the plurality of peak frequencies are not within the reference range, the determiner <NUM> determines that the adhesion of the oscillating AE sensor <NUM> is defective.

Adoption of this configuration eliminates the possibility of the adhesion of the oscillating AE sensor <NUM> being determined to be defective based only on one value which is not within the reference range due to noise mixing or the like. Therefore, it is possible to perform the determination more accurately.

The determiner <NUM> may also determine the adhesion state on the basis of a statistical value of peak frequencies. The statistical value is, for example, an average value, a mode value, a median value, or the like. When the adhesion state is determined on the basis of the average value of peak frequencies, the reference information storage <NUM> stores information of a reference range of the average value. In this case, the determiner <NUM> compares the average value of the peak frequencies with the reference range of the average value, determines that the adhesion of the oscillating AE sensor <NUM> is good when the average value of the peak frequencies is within the reference range of the average value, and determines that the adhesion of the oscillating AE sensor <NUM> is defective when the average value of the peak frequencies is not within the reference range of the average value.

The determiner <NUM> may also calculate respective distributions of frequencies of detection signals of AE sensors <NUM> which are obtained by oscillations of the AE sensors <NUM>, compare the calculated distributions, and determine that the adhesion of an AE sensor <NUM> is defective when a deviation of the calculated distribution of the AE sensor <NUM> from the distributions of values of the other AE sensors <NUM> is greater than or equal to a threshold value.

The signal processor <NUM> may be configured to output the determination result. In this case, the signal processor <NUM> further includes a display unit. The display unit displays the determination result of the determiner <NUM>. For example, the display unit may display information of the AE sensor <NUM> defectively adhered or may display the frequency distribution shown in <FIG>.

With this configuration, the user of the sensor adhesion state determination system <NUM> can easily find the AE sensor <NUM> defectively adhered.

In an embodiment, AE sensors have no oscillation function and detect elastic waves generated from the structure due to an external impact or load.

<FIG> is a diagram showing a system configuration of a sensor adhesion state determination system 100a according to the embodiment. The sensor adhesion state determination system 100a is used to determine the adhesion states of sensors adhered to a structure. In the present embodiment, a bridge will be described as an example of the structure, but the structure is not necessarily limited to a bridge.

The sensor adhesion state determination system 100a includes a plurality of AE sensors 10a-<NUM> to 10a-n and a signal processor 20a. The AE sensors 10a-<NUM> to 10a-n and the signal processor 20a are connected such that they can communicate with each other by wire or wirelessly. In the following description, the AE sensors 10a-<NUM> to 10a-n will be referred to as AE sensors 10a when they are not distinguished.

The AE sensors 10a are adhered to a surface of the structure which is to be subjected to deterioration evaluation with an adhesive or the like. For example, the AE sensors 10a are adhered to a concrete floor slab <NUM> of the bridge. Each of the AE sensors 10a has a detection function to detect elastic waves generated from the structure. The AE sensor 10a has a piezoelectric element, detects an elastic wave generated from a structure, and converts the detected elastic wave into a voltage signal (an AE source signal). The AE sensor 10a performs processing such as amplification and frequency limiting on the AE source signal and outputs the processed signal to the signal processor 20a. An acceleration sensor may be used instead of the AE sensor 10a. In this case, the acceleration sensor performs the same processing as that of the AE sensor 10a and outputs the processed signal to the signal processor 20a.

The signal processor 20a receives the AE source signals processed by the AE sensors 10a as inputs. The signal processor 20a determines the adhesion states of the AE sensors 10a on the basis of frequencies obtained from the input AE source signals. The signal processor 20a functions as a sensor adhesion state determination device. The signal processor 20a holds identification information of all AE sensors 10a connected to the signal processor 20a.

Next, the functional configuration of the signal processor 20a will be described.

The signal processor 20a includes a CPU, a memory, an auxiliary storage device, or the like connected via buses and executes an adhesion state determination program. By executing the adhesion state determination program, the signal processor 20a functions as a device including a calculator <NUM>, a reference information storage 202a, and a determiner 203a. It is to be noted that all or a part of each of the functions of the signal processor 20a may be realized using hardware such as an ASIC, a PLD, or an FPGA. The adhesion state determination program may be recorded in a computer readable recording medium. The computer readable recording medium is, for example, a storage device such as a flexible disk, a magneto-optical disk, a ROM, a portable medium such as a CD-ROM, or a hard disk installed in a computer system. The adhesion state determination program may also be transmitted and received via an electric communication line.

The signal processor 20a differs in configuration from the signal processor <NUM> in that the signal processor 20a includes a reference information storage 202a and a determiner 203a instead of the reference information storage <NUM> and the determiner <NUM>. The other components of the signal processor 20a are the same as those of the signal processor <NUM>. Therefore, the overall description of the signal processor 20a will be omitted and the reference information storage 202a and the determiner 203a will be described below.

The reference information storage 202a is configured using a storage device such as a magnetic hard disk device or a semiconductor storage device. The reference information storage 202a stores information of the resonance frequencies of sensors and reference values. The reference information storage 202a may store information of the resonance frequencies of sensors for each sensor type. Each of the reference values is a value serving as a reference from which it is possible to determine whether or not the adhesion is in a good state. The reference value may be set appropriately.

The determiner 203a determines the adhesion state of each of the AE sensors 10a on the basis of the peak frequencies of the AE sensors 10a calculated by the calculator <NUM>, the resonance frequency of the sensor, and the reference value.

<FIG> is a diagram showing an example of data obtained by measurement. The data of <FIG> is obtained by adhering two AE sensors 10a (sensor <NUM> and sensor <NUM>) to a surface of a structure and allowing the sensors <NUM> and <NUM> to detect elastic waves generated due to an external impact or load applied to the structure. In <FIG>, the horizontal axis represents time and the vertical axis represents the peak frequency. <FIG> is a plot of peak frequencies of AE source signals obtained when an impact is intermittently applied to the structure. In the example shown in <FIG>, the sensor <NUM> is in a good adhesion state and the sensor <NUM> is in a defective adhesion state.

As shown in <FIG>, the distributions of peak frequencies of the sensor <NUM> and the sensor <NUM> are different. The AE sensors 10a used here have a resonance frequency of <NUM>. Many elastic waves having peak frequencies in a range of <NUM> to <NUM> which are main frequencies of elastic waves propagating in the structure are seen in the distribution of the sensor <NUM> which is in a good adhesion state. On the other hand, in the distribution of the sensor <NUM>, only resonance components of the AE sensors 10a are dominant and peak frequencies of elastic waves concentrate around the resonance frequency of <NUM> of the AE sensors 10a. The signal processor 20a compares peak frequencies seen in the distribution of peak frequencies of elastic waves detected by each AE sensor 10a with the resonance frequency of the AE sensor 10a that has detected the elastic waves and determines that adhesion of the AE sensor 10a is defective when the comparison result is that the proportion of elastic waves having peak frequencies substantially matching the resonance frequency is greater than or equal to the reference value. Here, "substantially matching" indicates that the difference between the values compared (for example, the peak frequency and the resonance frequency) is ± several KHz (for example, ± <NUM>).

Further, defective adhesion can be detected more accurately by performing wavelet analysis. <FIG> shows the results of performing wavelet transformation on respective extracted waveforms of the sensors <NUM> and <NUM> of <FIG>. In <FIG>, the horizontal axis represents time and the vertical axis represents frequency. Specifically, <FIG> is a diagram showing a result of performing wavelet transformation on one waveform of the sensor <NUM> which is in a good adhesion state. <FIG> is a diagram showing a result of performing wavelet transformation on one waveform of the sensor <NUM> which is in a defective adhesion state. As shown in <FIG>, a region having various frequency components including the range of <NUM> to <NUM> which are main frequencies of elastic waves propagating in the structure is seen in the result of the sensor <NUM> which is in a good adhesion state (see circle <NUM> in <FIG>). On the other hand, as shown in <FIG>, the positions of peak frequencies of the sensor <NUM> which is defectively adhered are stable around the resonance frequency of the sensor, i.e., <NUM>, over the entire frequency range (see circle <NUM> in <FIG>). When elastic waves detected by the AE sensor 10a have been wavelet-transformed, the signal processor 20a can determine that adhesion of the AE sensor 10a is defective if the proportion of detected elastic waves having peak frequencies stable around the resonance frequency of the sensor is more than the reference value. That is, when the proportion of elastic waves whose peak frequencies substantially match the resonance frequency during a period of time greater than or equal to a predetermined ratio among the elastic waves detected by the AE sensor 10a is greater than or equal to the reference value, the determiner 203a can determine that adhesion of the AE sensor 10a is defective. Incidentally, the structure described with reference to <FIG> and <FIG> is concrete.

<FIG> is a flowchart showing a process flow for the signal processor 20a in the present embodiment. The description of <FIG> will be given with reference to the case in which elastic waves detected by one AE sensor 10a among a plurality of AE sensors 10a are used as an example. In <FIG>, the AE sensor 10a will be described as a first sensor.

The calculator <NUM> calculates peak frequencies from elastic waves obtained by the first sensor (step S201). The calculator <NUM> outputs the calculated peak frequencies to the determiner 203a. Next, the determiner 203a compares the calculated peak frequencies with the resonance frequency of the AE sensor 10a. For example, the determiner 203a determines whether or not the difference between each of the calculated peak frequencies and the resonance frequency of the sensor is less than or equal to a predetermined allowable value δ (for example, several kHz). Further, the determiner 203a calculates the count of elastic waves having a frequency difference of δ or less among the obtained elastic waves (step S202).

Then, the determiner 203a compares the calculated count with a reference value stored in the reference information storage 202a and determines whether or not the count is less than the reference value (step S203). When the count is less than the reference value (step S203: YES), the determiner 203a determines that adhesion of the first sensor is good (step S204). That is, the determiner 203a determines that the first sensor is in a good adhesion state.

On the other hand, when the count is greater than or equal to the reference value (step S203: NO), the determiner 203a determines that adhesion of the first sensor is defective (step S205). That is, the determiner 203a determines that the first sensor is in a defective adhesion state.

In the present embodiment, when the resonance frequency of the AE sensors 10a substantially match the peak frequencies of dominant elastic waves in the structure which is to be measured, adhesion of the sensors makes small differences in the peak frequencies. Therefore, it is preferable that the AE sensors 10a used in the present embodiment be those having a resonance frequency different from the peak frequencies of dominant elastic waves in the structure which is to be measured.

According to the sensor adhesion state determination system 100a configured as described above, the signal processor 20a calculates peak frequencies from AE source signals based on elastic waves detected by the AE sensors 10a. Then, the signal processor 20a compares the calculated peak frequencies with the resonance frequency of the AE sensors 10a, and determines that the adhesion state of an AE sensor 10a is good if the count of elastic waves whose peak frequencies differ from the resonance frequency of the AE sensor 10a by δ or less among elastic waves detected by the AE sensor 10a is less than the reference value and determines that the adhesion state of an AE sensor 10a is defective if the count of elastic waves whose peak frequencies differ from the resonance frequency of the AE sensor 10a by δ or less among elastic waves detected by the AE sensor 10a is greater than or equal to the reference value. Therefore, it is possible to determine the adhesion states of the sensors adhered to the structure.

According to at least of the embodiments described above, a plurality of sensors configured to detect elastic waves generated by a structure, a calculator configured to calculate peak frequencies of the elastic waves based on the elastic waves detected by the sensors, and a determiner configured to determine an adhesion state of each of the sensors by comparing the peak frequencies with information which is a reference for determination as to whether or not each of the sensors is in a good adhesion state are provided and thus it is possible to determine the adhesion state of each of the sensors adhered to the structure.

Claim 1:
A sensor adhesion state determination system (<NUM>, 100a) comprising:
a plurality of sensors (<NUM>-<NUM>~<NUM>-n, 10a-<NUM>~10a-n) adhered to a structure (<NUM>) and configured to detect elastic waves;
a calculator (<NUM>) configured to calculate peak frequencies of the elastic waves on the basis of the elastic waves detected by the plurality of sensors; and
a determiner (<NUM>, 203a) configured to determine an adhesion state of each of the plurality of sensors (<NUM>-<NUM>~<NUM>-n, 10a-<NUM>~10a-n) by comparing the peak frequencies with information serving as a determination reference,
characterized in that
the determiner (203a) is configured to calculate a count of elastic waves for a sensor, the count of elastic waves being the number of waves for which the difference between the calculated peak frequencies and resonant frequency of the sensor is less than or equal to a predetermined value, the determiner (203a) being configured to determine that the adhesion of the sensor (<NUM>-<NUM>~<NUM>-n, 10a-<NUM>~10a-n) to the structure (<NUM>) is defective when the count of elastic waves is greater than or equal to a predetermined count,
or,
wherein each of the plurality of sensors (<NUM>-<NUM>~<NUM>-n, 10a-<NUM>~10a-n) has an oscillation function to oscillate at a specific frequency, and
wherein the calculator (<NUM>) is configured to calculate a peak frequency of an elastic wave generated by oscillation on the basis of the elastic wave output from a sensor that has detected the elastic wave,
wherein the determiner (<NUM>) is configured to determine that adhesion of an oscillating sensor is defective when some or all of the peak frequencies of the elastic waves are not within a range of peak frequencies serving as the determination reference.