Zhang et al 2020a - Revision history

Rimni at 13:39, 2 March 2021

2021-03-02T13:39:18Z

Rimni: /* 4. Experimental verification based on scaning and paint system (SPS) */

2021-03-02T13:38:12Z

‎4. Experimental verification based on scaning and paint system (SPS)

Ennlia at 01:31, 5 October 2020

2020-10-05T01:31:12Z

Ennlia at 03:52, 3 October 2020

2020-10-03T03:52:52Z

Ennlia at 03:12, 3 October 2020

2020-10-03T03:12:49Z

Rimni at 10:12, 1 October 2020

2020-10-01T10:12:24Z

Rimni at 10:07, 1 October 2020

2020-10-01T10:07:40Z

Rimni at 10:04, 1 October 2020

2020-10-01T10:04:11Z

Rimni at 10:01, 1 October 2020

2020-10-01T10:01:08Z

Rimni at 09:47, 1 October 2020

2020-10-01T09:47:09Z

@@ Line 411: / Line 411: @@
 ==5. Noise reduction test ==
-Based on the aforementioned findings, spectrum of radiated noise from the forklift is mainly concentrated in low frequency. The de-noising test adopted sound-absorbing materials with high absorption coefficient in low frequency band, and the materials were covered the whole top of engine cabin exhibited in [[#img-5|Figure 5]]  [26]. According to the test standard of JB/T3300-2010, the noise radiation results of at the modes of lifting, operating and idling are respectively achieved and described in Tables [[#tab-3|3]] to [[#tab-5|5]].
+Based on the aforementioned findings, spectrum of radiated noise from the forklift is mainly concentrated in low frequency. The de-noising test adopted sound-absorbing materials with high absorption coefficient in low frequency band, and the materials were covered the whole top of engine cabin exhibited in [[#img-5|Figure 5]]  [26]. According to the test standard of JB/T3300-2010, the noise radiation results of at the modes of lifting, operating and idling are respectively achieved and described in  [[#tab-3|Tables 3]] to [[#tab-5|5]].
 <div id='img-5'></div>

@@ Line 387: / Line 387: @@
 In order to verify the accuracy of numerical results, SPS is adopted for the noise source identification test on the same prototype. The basic measurement process of the system is as follows: while a PU probe is scanning, firstly, the camera tracks the moving trajectory of the probe and forms the graphic grids; and then the system software automatically synchronizes the audio and video signals and output the measured values directly; lastly, key acoustic parameters can be figured out including pressure and intensity of sound field, which are further presented with high-resolution nephogram.
-The identification test is carried out in parallel with the noise acquisition test, namely, the test conditions are both idle speed of 671 rpm [20]. The positions of the acoustic scanning are at the top of the open engine cabin and the exhaust pipe. Through the scanning test, the main acoustic parameters mentioned above can be calculated. The results obtained by post processing are illustrated in Figures [[#img-3|3]]  and [[#img-4|4]].
+The identification test is carried out in parallel with the noise acquisition test, namely, the test conditions are both idle speed of 671 rpm [20]. The positions of the acoustic scanning are at the top of the open engine cabin and the exhaust pipe. Through the scanning test, the main acoustic parameters mentioned above can be calculated. The results obtained by post processing are illustrated in  [[#img-3|Figures 3]]  and [[#img-4|4]].
 <div id='img-3'></div>
@@ Line 407: / Line 407: @@
-Figures [[#img-3|3]]  and [[#img-4|4]] indicate that noise energies radiated from the engine cabin and exhaust pipe mainly concentrate in the low-frequency band. The peak spectrum is generated by frequency component 23.44Hz, in which the relative error between 23.44Hz and the calculated value (22.44 Hz) is less than 5%. It proves that the proposed method for noise source separation based on the improved FastICA algorithm is of high accuracy and has important practical application.
+[[#img-3|Figures 3]]  and [[#img-4|4]] indicate that noise energies radiated from the engine cabin and exhaust pipe mainly concentrate in the low-frequency band. The peak spectrum is generated by frequency component 23.44Hz, in which the relative error between 23.44Hz and the calculated value (22.44 Hz) is less than 5%. It proves that the proposed method for noise source separation based on the improved FastICA algorithm is of high accuracy and has important practical application.
 ==5. Noise reduction test ==

@@ Line 4: / Line 4: @@
 ==A noise reduction method for forklift based on numerical separation and spectrum analysis of main noise sources==
-Enlai Zhang<sup>1,2</sup>, Liang Hou<sup>3</sup>, Jianming Zhuo<sup>2</sup> and Tao Guo<sup>4</sup>
+'''Enlai Zhang*<sup>1,2</sup>, Liang Hou<sup>3</sup>, Jianming Zhuo<sup>2</sup>, Jianming Zhuo'''<sup>2</sup>''', Tao Guo<sup>4</sup> '''
 <sup>1</sup>Chengyi University College, Jimei University, Xiamen;

@@ Line 4: / Line 4: @@
 ==A noise reduction method for forklift based on numerical separation and spectrum analysis of main noise sources==
-Enlai Zhang<sup>1,2</sup>, Liang Hou<sup>3</sup>, Jianming Zhuo<sup>2</sup>, Zhanfu Li<sup>4</sup> and Tao Guo<sup>5</sup>
+Enlai Zhang<sup>1,2</sup>, Liang Hou<sup>3</sup>, Jianming Zhuo<sup>2</sup> and Tao Guo<sup>4</sup>
-<sup>1</sup>Chengyi University College, Jimei University, Xiamen
+<sup>1</sup>Chengyi University College, Jimei University, Xiamen;
-<sup>2</sup>Xiamen King Long United Automotive Industry Co., Ltd, Xiamen
+<sup>2</sup>Xiamen King Long United Automotive Industry Co., Ltd, Xiamen;
-<sup>3</sup>School of Aerospace Engineering, Xiamen University, Xiamen
+<sup>3</sup>School of Aerospace Engineering, Xiamen University, Xiamen;
-<sup>4</sup>LiCSCEC Strait Construction and Development Co., Ltd, Fuzhou
+<sup>4</sup>Engineering Technology Research Institute, Xiamen XGMA Machinery Co., Ltd, Xiamen;
-−
-<sup>5</sup>Engineering Technology Research Institute, Xiamen XGMA Machinery Co., Ltd, Xiamen
 '''<sup>*</sup>'''[mailto:E-mail:zhangenlai1986@163.com E-mail:zhangenlai1986@163.com]

@@ Line 4: / Line 4: @@
 ==A noise reduction method for forklift based on numerical separation and spectrum analysis of main noise sources==
-Enlai Zhang<sup>1,2</sup>, Liang Hou<sup>3</sup>, Jianming Zhuo<sup>2</sup> and Tao Guo<sup>4</sup>
+Enlai Zhang<sup>1,2</sup>, Liang Hou<sup>3</sup>, Jianming Zhuo<sup>2</sup>, Zhanfu Li<sup>4</sup> and Tao Guo<sup>5</sup>
 <sup>1</sup>Chengyi University College, Jimei University, Xiamen
@@ Line 12: / Line 12: @@
 <sup>3</sup>School of Aerospace Engineering, Xiamen University, Xiamen
-<sup>4</sup>Engineering Technology Research Institute, Xiamen XGMA Machinery Co., Ltd, Xiamen
+<sup>4</sup>LiCSCEC Strait Construction and Development Co., Ltd, Fuzhou
 '''<sup>*</sup>'''[mailto:E-mail:zhangenlai1986@163.com E-mail:zhangenlai1986@163.com]

@@ Line 92: / Line 92: @@
 <li> If the algorithm converges, all EICs <math>\bf Y</math> can be figured out based on the separation matrix <math>\bf W</math> and Eq. (2).</li>
-As to the uncertainty of EICs in order, phase and amplitude, this paper presents the method to correct the EICs by written MATLAB program based on fast fourier transform (FFT) and maximum spectrum similarity (MSS)[18]. The main idea is as below: Firstly, the spectral characteristics of the acquired observation signals and EICs are analyzed; secondly, the difference between the maximum and minimum spectral amplitudes of the current signals is divided into <math>m</math> groups for figuring out the spectrum similarity; furthermore, the maximum similarity value is regarded as the EIC from the corresponding channel. Finally, the uncertainty of EICs is corrected in terms of phase and amplitude. The specific implementation process is as below:
+As to the uncertainty of EICs in order, phase and amplitude, this paper presents the method to correct the EICs by written MATLAB program based on fast fourier transform (FFT) and maximum spectrum similarity (MSS)[18]. The main idea is as below: Firstly, the spectral characteristics of the acquired observation signals and EICs are analyzed; secondly, the difference between the maximum and minimum spectral amplitudes of the current signals is divided into <math>m</math> groups for figuring out the spectrum similarity; furthermore, the maximum similarity value is regarded as the EIC from the corresponding channel. Finally, the uncertainty of EICs is corrected in terms of phase and amplitude. The specific implementation process is as below.
 <li> Applying FFT to the <math>k</math>th channel of observed signal <math>(x_k)</math> and the <math>k</math>th EIC <math>(y_k)</math> obtained by traditional FastICA algorithm;</li>

@@ Line 345: / Line 345: @@
 The next work is to identify the two main noise sources and spectrum analysis. First of all, it is necessary to point out that the noise signal from engine cabin of the forklift is a complex wideband signal. Therefore, it is difficult to recognize noise accurately only by FFT analysis, and the time-domain information of acoustic signal needs to be considered simultaneously [20].
-The previous study indicated that the Morlet wavelet was widely used in time-domain analysis, which is defined as[22-24]:
+The previous study indicated that the Morlet wavelet was widely used in time-domain analysis, which is defined as [22-24]:
 {| class="formulaSCP" style="width: 100%; text-align: center;"
@@ Line 359: / Line 359: @@
 where <math>f_b</math> and <math>f_c</math> are the bandwidth parameter and the center frequency, respectively. In this case, <math>f_b=2</math> and <math>f_c=1.5</math>.
-The radiated noise energy of the vehicle changes over time, which can be reflected by the change of the crank angle. Generally, the ignition time of an engine is set based on the crank angle. When the piston motion moves to the top dead center, the crank angle is 0 °CA. With the use of FFT and continuous Morlet wavelet transform (MWT), spectrum analyses were carried out on the two EICs [20]. The results are shown in Figures [[#img-1|1]]   and [[#img-2|2]] .
+The radiated noise energy of the vehicle changes over time, which can be reflected by the change of the crank angle. Generally, the ignition time of an engine is set based on the crank angle. When the piston motion moves to the top dead center, the crank angle is 0 °CA. With the use of FFT and continuous Morlet wavelet transform (MWT), spectrum analyses were carried out on the two EICs [20]. The results are shown in Figures [[#img-1|1]]   and [[#img-2|2]].
 [[#img-1|Figure 1]]  presents that the major energy of independent component <math>y_2</math> concentrates in the lower frequency band; the peak spectrum appears in the frequency of 22.44Hz, and the twice frequency, 44.88Hz, takes the second place; the waveform of <math>y_2</math> and the change of crank angle indicate that <math>y_2</math> is periodic; the information of time domain also shows that the peak value of 22.44Hz appears at zero, 180 and 360 degrees of crank angle. In this test, the engine is 4-cylinder 4-stroke, and its operating condition is idle speed, i.e. 671 rpm. The formula for calculating the fundamental frequency of engine combustion can be expressed as <math>f=vl/(60t)</math> [25], here, <math>v</math> is the running speed of the engine, <math>l</math> and <math>t</math> represent the number of cylinders and stroke coefficient respectively, and <math>t=2</math> for the four strokes. The ignition frequency of the engine at this moment can be calculated as 22.37 Hz by Eq.(11). The second harmonic frequency of the fundamental frequency is 44.88 Hz, and the ignition interval angle is 720 0°CA/4=180 0°CA. Therefore, it can be determined that the base frequency noise of the engine combustion corresponds to the independent component <math>y_2</math>.
@@ Line 381: / Line 381: @@
-It can be found that, in [[#img-2|Figure 2]], the energy of <math>y_6</math> is peaked at 22.44Hz, and the variations of the waveform and crankshaft indicate that <math>y_6</math> is also periodic. The base frequency generated by the exhaust system of the diesel engine is 22.44 Hz, which is consistent with the fundamental frequency of the combustion noise [25]. In the test, the exhaust fundamental-frequency noise of the prototype is generated about 20 °CA after the top dead center, corresponding to [[#img-2|Figure 2]](c). Thus, the independent component <math>y_6</math> is the exhaust fundamental-frequency noise
+It can be found that, in [[#img-2|Figure 2]], the energy of <math>y_6</math> is peaked at 22.44Hz, and the variations of the waveform and crankshaft indicate that <math>y_6</math> is also periodic. The base frequency generated by the exhaust system of the diesel engine is 22.44 Hz, which is consistent with the fundamental frequency of the combustion noise [25]. In the test, the exhaust fundamental-frequency noise of the prototype is generated about 20 °CA after the top dead center, corresponding to [[#img-2|Figure 2]](c). Thus, the independent component <math>y_6</math> is the exhaust fundamental-frequency noise.
 ==4. Experimental verification based on scaning and paint system (SPS)==

@@ Line 31: / Line 31: @@
 ==2. Improved FastICA algorithm==
-Assuming that the time difference between signal sources arriving at the sensor is not considered, there are ''n'' signal sources with unknown statistical independents and ''m'' sensors (<math>n, m \ge 2</math>), and the observation signals received by sensors are formed by linear mixing of the source signals, which is expressed as [3,11]
+Assuming that the time difference between signal sources arriving at the sensor is not considered, there are <math>n</math> signal sources with unknown statistical independents and <math>m</math> sensors (<math>n, m \ge 2</math>), and the observation signals received by sensors are formed by linear mixing of the source signals, which is expressed as [3,11]
 {| class="formulaSCP" style="width: 100%; text-align: center;"
@@ Line 92: / Line 92: @@
 <li> If the algorithm converges, all EICs <math>\bf Y</math> can be figured out based on the separation matrix <math>\bf W</math> and Eq. (2).</li>
-As to the uncertainty of EICs in order, phase and amplitude, this paper presents the method to correct the EICs by written MATLAB program based on fast fourier transform (FFT) and maximum spectrum similarity (MSS)[18]. The main idea is as below: Firstly, the spectral characteristics of the acquired observation signals and EICs are analyzed; secondly, the difference between the maximum and minimum spectral amplitudes of the current signals is divided into ''m'' groups for figuring out the spectrum similarity; furthermore, the maximum similarity value is regarded as the EIC from the corresponding channel. Finally, the uncertainty of EICs is corrected in terms of phase and amplitude. The specific implementation process is as below:
+As to the uncertainty of EICs in order, phase and amplitude, this paper presents the method to correct the EICs by written MATLAB program based on fast fourier transform (FFT) and maximum spectrum similarity (MSS)[18]. The main idea is as below: Firstly, the spectral characteristics of the acquired observation signals and EICs are analyzed; secondly, the difference between the maximum and minimum spectral amplitudes of the current signals is divided into <math>m</math> groups for figuring out the spectrum similarity; furthermore, the maximum similarity value is regarded as the EIC from the corresponding channel. Finally, the uncertainty of EICs is corrected in terms of phase and amplitude. The specific implementation process is as below:
 <li> Applying FFT to the <math>k</math>th channel of observed signal <math>(x_k)</math> and the <math>k</math>th EIC <math>(y_k)</math> obtained by traditional FastICA algorithm;</li>
@@ Line 115: / Line 115: @@
 <li> Adjusting the amplitude. Among the EICs having been ordered, a component spectrum with relative high amplitude and unique frequency is to be chosen as a benchmark. And then, the EIC is multiplied by appropriate weight factor so that the EIC under this frequency and the observed signal have the same spectrum amplitude. Therefore, the EIC is considered to be the corresponding source signal with consistent amplitude.</li>
 ==3. Main noise sources and spectrum analysis==

@@ Line 511: / Line 511: @@
 According to the noise limit standard of JB/T2391-2007, the total radiated sound power level of the prototype is calculated to be 104.13 dB(A). Compared to the decibel level of 105.88 dB(A) before the modification, the level has been reduced by 1.75 dB(A), which is basically satisfy the domestic noise radiation requirement in the forklift industry and recognized by the cooperative enterprise(XGMA).
-=6. Conclusion =
+==6. Conclusion==
 Focused on the noise reduction, this paper takes the frequency spectral characteristics of the primary and secondary noise sources as the starting point. The separation and identification of main noise sources are important prerequisites for this study. In view of the uncertainties of the EICs, this paper presents the improved FastICA algorithm based on FFT and MSS and combines MWT technique to identify and calculate the primary and secondary noise sources of the forklift during the idle condition. With the use of SPS, the noise source scanning experiment is conducted on the same prototype and condition. The error between the experiment result and the calculated result by the algorithm is less than 5%, which verifies the accuracy and reliability of the improved FastICA algorithm. Furthermore, the spectral analysis of the main noise sources indicates that the radiated noise level of the forklift depends on the low frequency. Through the low frequency sound absorption improvement test and the standard test on the prototype, it shows that the level of prototype’s radiated sound power reduces by 1.75 dB(A), which demonstrates that a satisfactory effect on noise reduction is achieved. Finally, the effective noise reduction method proposed in this paper can also be  tried and applied for different models of engineering vehicles and other working conditions.
-'''Fundings:''' The work was supported by the Natural Science Foundation of Fujian Province (2018J01483), National Natural Science Foundation of China (12004136),  China Postdoctoral Science Foundation (2019M662252) and Doctoral Scientific Research Foundation of Chengyi University College (CK17063).
+==Fundings==
-'''Acknowledgments:''' The work was also supported by Outstanding Young Scientific Research Talents Cultivation Plan of Fujian Universities, and thanks to the engineering technology research institute of Xiamen XGMA Machinery Co., Ltd for providing test vehicle.
+The work was supported by the Natural Science Foundation of Fujian Province (2018J01483), National Natural Science Foundation of China (12004136),  China Postdoctoral Science Foundation (2019M662252) and Doctoral Scientific Research Foundation of Chengyi University College (CK17063).
-==References ==
+==Acknowledgments==
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@@ Line 421: / Line 421: @@
-<div id="_GoBack" class="center" style="width: auto; margin-left: auto; margin-right: auto;">
+<div class="center" style="font-size: 75%;">
- [[Image:Review_405164275504-image25.jpeg|402px]] </div>
-−
-<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">
-'''Figure 5.''' Arrangement of sound-absorbing materials</div>
-−
-<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">
 '''Table 3'''. Noise radiation level of operating mode (dB(A))</div>
-{| style="width: 86%;margin: 1em auto 0.1em auto;border-collapse: collapse;"
+<div id='tab-3'></div>
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;vertical-align: top;"|Test number
+|  style="text-align: center;vertical-align: top;"|1
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;vertical-align: top;"|Measuring point 1
+|  style="text-align: center;"|82.4
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;vertical-align: top;"|Measuring point 2
+|  style="text-align: center;"|80.6
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;vertical-align: top;"|Measuring point 3
+|  style="text-align: center;vertical-align: top;"|80.1
-|-
-|  style="border-top: 1pt solid black;text-align: center;vertical-align: top;"|1
-|  style="border-top: 1pt solid black;text-align: center;"|82.4
-|  style="border-top: 1pt solid black;text-align: center;"|80.6
-|  style="border-top: 1pt solid black;text-align: center;vertical-align: top;"|80.1
 |-
 |  style="text-align: center;vertical-align: top;"|2
@@ Line 452: / Line 444: @@
 |  style="text-align: center;vertical-align: top;"|79
 |-
-|  style="border-bottom: 1pt solid black;text-align: center;vertical-align: top;"|4
+|  style="text-align: center;vertical-align: top;"|4
-|  style="border-bottom: 1pt solid black;text-align: center;"|82.8
+|  style="text-align: center;"|82.8
-|  style="border-bottom: 1pt solid black;text-align: center;"|82.3
+|  style="text-align: center;"|82.3
-|  style="border-bottom: 1pt solid black;text-align: center;vertical-align: top;"|83.7
+|  style="text-align: center;vertical-align: top;"|83.7
 |}
-<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">
+<div class="center" style="font-size: 75%;">
 '''Table 4'''. Noise radiation level of lifting mode (dB(A))</div>
-{| style="width: 86%;margin: 1em auto 0.1em auto;border-collapse: collapse;"
+{| class="wikitable" style="margin: 1em auto 0.1em auto;border-collapse: collapse;font-size:85%;width:auto;"
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;vertical-align: top;"|Test number
+|  style="text-align: center;vertical-align: top;"|1
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;vertical-align: top;"|Measuring point 1
+|  style="text-align: center;"|79.6
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;vertical-align: top;"|Measuring point 2
+|  style="text-align: center;"|79.5
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;vertical-align: top;"|Measuring point 3
+|  style="text-align: center;vertical-align: top;"|79.3
-|-
-|  style="border-top: 1pt solid black;text-align: center;vertical-align: top;"|1
-|  style="border-top: 1pt solid black;text-align: center;"|79.6
-|  style="border-top: 1pt solid black;text-align: center;"|79.5
-|  style="border-top: 1pt solid black;text-align: center;vertical-align: top;"|79.3
 |-
 |  style="text-align: center;vertical-align: top;"|2
@@ Line 484: / Line 473: @@
 |  style="text-align: center;vertical-align: top;"|78.1
 |-
-|  style="border-bottom: 1pt solid black;text-align: center;vertical-align: top;"|4
+|  style="text-align: center;vertical-align: top;"|4
-|  style="border-bottom: 1pt solid black;text-align: center;"|80.6
+|  style="text-align: center;"|80.6
-|  style="border-bottom: 1pt solid black;text-align: center;"|79.4
+|  style="text-align: center;"|79.4
-|  style="border-bottom: 1pt solid black;text-align: center;vertical-align: top;"|78.8
+|  style="text-align: center;vertical-align: top;"|78.8
 |}
-<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">
+<div class="center" style="font-size: 75%;">
 '''Table 5'''. Noise radiation level of idling mode (dB(A))</div>
-{| style="width: 86%;margin: 1em auto 0.1em auto;border-collapse: collapse;"
+{| class="wikitable" style="margin: 1em auto 0.1em auto;border-collapse: collapse;font-size:85%;width:auto;"
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;vertical-align: top;"|Test number
+|  style="text-align: center;vertical-align: top;"|1
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;vertical-align: top;"|Measuring point 1
+|  style="text-align: center;"|64.2
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;vertical-align: top;"|Measuring point 2
+|  style="text-align: center;"|63.1
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;vertical-align: top;"|Measuring point 3
+|  style="text-align: center;vertical-align: top;"|63.7
-|-
-|  style="border-top: 1pt solid black;text-align: center;vertical-align: top;"|1
-|  style="border-top: 1pt solid black;text-align: center;"|64.2
-|  style="border-top: 1pt solid black;text-align: center;"|63.1
-|  style="border-top: 1pt solid black;text-align: center;vertical-align: top;"|63.7
 |-
 |  style="text-align: center;vertical-align: top;"|2
@@ Line 516: / Line 502: @@
 |  style="text-align: center;vertical-align: top;"|61.7
 |-
-|  style="border-bottom: 1pt solid black;text-align: center;vertical-align: top;"|4
+|  style="text-align: center;vertical-align: top;"|4
-|  style="border-bottom: 1pt solid black;text-align: center;"|63.6
+|  style="text-align: center;"|63.6
-|  style="border-bottom: 1pt solid black;text-align: center;"|62.9
+|  style="text-align: center;"|62.9
-|  style="border-bottom: 1pt solid black;text-align: center;vertical-align: top;"|64.2
+|  style="text-align: center;vertical-align: top;"|64.2
 |}