Juanxiu Hu

Dongxian He

Po Yang

Study on Plant Nutrition Indicator Using Leaf Spectral Transmittance for Nitrogen Detection

Keywords: Chlorophyll content, Nitrogen content, Spectral feature parameter, Spectral transmittance 1

The low fertilizer utilization at growing season and environment pollution coursed by unreasonable fertilization are becoming global outstanding problems in agricultural production. Scientific and reasonable fertilization based on rapid and nondestructive plant nutrient detection will be a valuable solution for solving above problems. In this study, spectral transmittance in wavelength ranged from 300 to 1100 nm, chlorophyll content and nitrogen content of rice and cucumber leaves treated with culture solution in five different nitrogen levels were measured. According to the correlation analysis between them, 560, 650, and 720 nm as feature wavelengths and 940 nm as reference wavelength were determined for nitrogen detection. Correlation analysis between 21 spectral feature parameters composed by the transmittance at above wavelength, the leaf chlorophyll content and nitrogen content, and combined with their regression examination indicated that spectral feature parameters of (T940 -T560) / (T940 + T560), log (T940/T560) and log (T940/T650) are useful to conduct plant nutrient diagnosis with less than 8% relative error in rice and cucumber leaves. Therefore, the above spectral feature parameters as plant nitrogen indicators can be used to estimate the chlorophyll content and nitrogen content, furthermore support for non-destructive plant nutrient detection and fertilizer recommendation based on testing soil.

Introduction

Nitrogen is an essential nutrient factor for growth and quality of plants. The applying of nitrogen fertilizer has been growing persistently in order to increase the crop yield. However, the environmental pollutions caused by low seasonal utilization of nitrogen fertilizer and the unreasonable applications of chemical fertilizer have become outstanding issues in agricultural production [START_REF] Zeng | Fertilizer Application and its Effect on Grain Production in Different Counties of China (in Chinese)[END_REF][START_REF] Ju | Reducing Environmental Risk by Improving N Management in Intensive Chinese Agricultural Systems[END_REF]. The amount of nitrogen in soil is closely related to the nitrogen nutrition status of plant. The scientific nitrogen fertilization based on evaluating nitrogen nutrition status of plant is an effective approach to solve the problem above.

Chlorophyll content and nitrogen content are important indications for evaluating nitrogen nutrition status of plant. Estimating chlorophyll content and nitrogen content by methods of chemical analysis though presenting reliable results, cannot meet the requirement of guiding fertilizer promptly because of the vary complicated and time-consuming process [START_REF] Jiao | Progress and Perspective on Nutrition Diagnosis of Plant Nitrogen (in Chinese)[END_REF]. The application of remote sensing technology in agriculture has provided a new means for rapid and non-destructive diagnosis of plant nitrogen nutrition [START_REF] Tian | Non-destructive Monitoring of Crop Nitrogen Nutrition Based on Spectral Information (in Chinese)[END_REF]. There are lots of studies focusing on diagnosis of plant nitrogen nutrition based on the spectral properties of plant were carried out. Spectral reflectance of plant leaves in the vicinity of 550 and 670 nm were highly relevant to chlorophyll and nitrogen contents by measuring the reflectance of leaves or canopy [START_REF] Thomas | Estimating Nitrogen Content of Sweet Pepper Leaves by Reflectance Measurements[END_REF][START_REF] Filella | Evaluating Wheat Nitrogen Status with Canopy Reflectance Indices and Discriminant Analysis[END_REF]. Reflectance in 550 nm and 710 nm could be used to better estimate the nitrogen content in corn and R(550~600) / R(800~900) could be sensitive to reflect the nitrogen stress in corn. The linear combinations of reflectance in 620 and 760 nm had good correlation with nitrogen contents in rice leaves [START_REF] Shibayama | A Spectroradiometer for Field Use VII. Radiometric Estimation of Nitrogen Levels in Field Rice Canopy[END_REF].

As to studies on plant spectral transmittance and nitrogen nutrition, an in-vivo detection device was developed for estimation plant chlorophyll content based on the difference in leaf spectral transmittance between in 660-690 nm and in 760-1100 nm [START_REF] Watanabe | Method of and Device for Measuring Chlorophyll of Living Leaves[END_REF]. Spectral characteristic parameter log (I660-690/I760-1100) was adopted in the device to estimate leaf chlorophyll content, in which I660-690 and I760~1100 were the intensity of light within corresponding wavebands having transmitted through leaves. Minolta, a Japanese company, then improved the algorithm and developed a portable chlorophyll meter. The spectral feature parameter adopted in the meter was log (T940/T650), namely, SPAD (Soil and Plant Analysis Development) value, in which T940 and T650 were the transmittance in corresponding wavelengths. The portable chlorophyll meter has been widely applied in rapid and non-destructive diagnosis of nitrogen nutrition in rice, maize, wheat, cotton and other field corps [START_REF] Turner | Chlorophyll Meter to Predict Nitrogen Topdress Requirement for Semidwarf Rice[END_REF][START_REF] Wu | Nitrogen Fertilizer Application Based on the Diagnosis of Nitrogen Nutrition of Rice Plants (Oryza sativa L.) Using Chlorophyll Meter (in Chinese)[END_REF][START_REF] Piekielek | Use of a Chlorophyll Meter to Predict Side-dress Nitrogen Requirements for Maize[END_REF][START_REF] Follett | Use of a Chlorophyll Meter to Evaluate the Nitrogen Status of Dryland Winter Wheat[END_REF]. It also had a good performance in application for horticultural plants and xylophyta such as sweet potato, rape, peach, and maple, etc [START_REF] Shaahan | Predicting Nitrogen, Magnesium and Iron Nutritional Status in some Perennial Crops Using a Portable Chlorophyll Meter[END_REF][START_REF] Chang | Nondestructive and Rapid Estimation of Hardwood Foliar Nitrogen Status Using the SPAD-502 Chlorophyll Meter[END_REF][START_REF] Li | Study on Relation between SPAD Value and Chlorophyll Contents in 10 Kinds Horticulture Woody Plants (in Chinese)[END_REF][START_REF] Su | Study on Correlations between SPAD Readings and Chlorophyll Content in Leaves of Sweet Potato, Dioscorea and Konjaku (in Chinese)[END_REF].

Many studies have been conducted on nitrogen nutrition diagnosis with the plant reflection spectrum, while the research on plant spectral transmittance is mostly based on the application of the portable chlorophyll meter. In this study, the feature band in direct relation with nitrogen nutrition of plants was determined based on transmittance spectra of rice and cucumber leaves cultivated in nutrient solution of different levels of nitrogen fertilizer. The correlation analysis and regression estimation error analysis were carried out of established spectral feature parameters and chlorophyll content as well as nitrogen contents, so as to determine plant nutrition indicators for nitrogen detection through estimating chlorophyll content and nitrogen content in leaves.

2

Materials and Methods

Cultivation of Plants

In this study, the cultivation of plants were performed in a environment controlled greenhouse using cucumber (Cucumis sativus L., cv., Zhongnong No.8 and Beijing 203) and rice (Oryza sativa L., cv., Wuyujing No. 3) as model plant during the period from 2008 to 2009. Cucumber and rice seedlings were transplanted to 5 cultivation beds of the same dimension (250 cm × 60 cm × 40 cm), and 16 plants in each bed. A homogenous mixture of vermiculite, grass peat and perlite at a proportion of 3:1:1 was used as the cultivation base. The temperature and light intensity in greenhouse were optimumly controlled through pad and fan cooling system and sunshade screen.

Fig. 1 The status of cucumber and rice cultivation.

Cucumber and rice plants were treated with culture solution in 5 different nitrogen levels. With the nitrogen level of nutrient solution formulation for cucumbers from Yamazaki Japan as 100%, content of nitrogen in the nutrient solutions were increased or decreased by 50% and 100% respectively while content equality of other major nutrient elements was ensured as far as possible (Table 1). Then the growing areas, according to the different nitrogen levels, were marked as N0, N50, N100, N150 and N200. The pH of culture solution was adjusted to 6.5-7.5. Pumps and timers were applied in automatic timing irrigation of the nutrient solution.

Measured Parameters and Measurement Methods

During the tillering stage of rice and fast growth period of cucumber, five samples of plant leaves in each treatment area were selected respectively for parameters measurement of the spectral property and nitrogen nutrition indices, including the spectral transmittance of leaf within the wavelength range of 300-1100 nm, and the chlorophyll content and nitrogen content of leaf.

(1) The determination of spectral transmittance Spectral transmittance of rice or cucumber leaves was determined using an integrating sphere measurement device in the large sample chamber of spectrophotometer (UV3150, Shimadzu Co., Japan). The measurement wavelength range was 300-1100 nm with the interval of 1nm. The light source switch point was set at 360 nm and the raster switch point at 820 nm while the slit width was 20 nm. The measurement position was basically in both sides of the main vein and middle of the leaf. For each leaf, 4 different positions were selected for determination of spectral transmittance, so each leaf was measured for 4 times.

(2) The determination of chlorophyll content 0.05g rice leaves (0.20g cucumber leaves) were weighed, cut into filaments and put into test tubes. After adding 10mL mixture of 1:1 ethanol-acetone, the tubes were placed in refrigerator at 4 o C for 16-18h in the dark for extracting chlorophyll of leaves. The extract was uniformly shaken and then transferred into a clean cell for determination of chlorophyll content. With the ethanol-acetone (1:1) mixture as the reference, absorbance values at 663 and 645nm of the extract were measured by a spectrometer (UV3150, Shimadzu Co., Japan).The chlorophyll content was calculated by the correction formula of Arnon method [START_REF] Lichtenthaler | Determinations of Total Carotenoids and Chlorophyll a and b Leaf Extracts in Different Solvents[END_REF].

(3) The determination of nitrogen content 0.10g rice or cucumber leaves were weighed to a 100mL digestion tubes and wetted with a little water dripped in, and then 4mL of concentrated H 2 SO 4 was added. The tubes were covered with small curved neck funnels to place overnight after shaken gently. The next day, the tubes were put into a digestion furnace to heat firstly at 180 o C for an hour, and then at 300 o C for 2 hours. When the solution became brown-red, the tubes were moved away and cooled for 3min. Then 15 drops of 30% H 2 O 2 were added into the solution. The tube was put into the digestion furnace to heat at 300 o C again for 15 min after shaken. Then 10 drops of 30% H 2 O 2 was added after the tubes were moved away to cool for 3min again. The above procedures were repeated for times with the amount of 30% H 2 O 2 gradually decreased until the solution became colorless or bright. Finally, the tubes were heated for 20min in order to remove residual H 2 O 2 . After thoroughly cooled, the digestion solution was arranged on a Kjeldahl type automatic azotometer (KDY-9830, Beijing Tongrunyuan Electromechanical Technology Co., Ltd., China) for distillation titration to determine the nitrogen content.

Processing and Analysis of Data

The mean value of four measurements of each leaf sample's spectral transmittance within 300-1100 nm was adopted. The correlation analysis of the leaf spectral transmittance, the chlorophyll content, and the nitrogen content was conducted with the SPSS software, so as to determine the feature wavelength and reference wavelength for nitrogen nutrition diagnosis in plants. With the correlation analysis of spectral feature parameters established by the spectral transmittance of characteristic wavelength and reference wavelength, the leaf chlorophyll content, and the leaf nitrogen contents, plant nitrogen nutrition indicators were determined and tested. Spectral transmittances of cucumber and rice leaves were changed basically with the same trend of the wavelengths in different nitrogen nutrition levels. In the range of photosynthetically active radiation wave band (400-700 nm), the leaf spectral transmittance formed wave crest at 550 nm and shaped up wave trough at 680 nm. At the same time, the leaf spectral transmittance reduced with the increasing nitrogen. Spectral transmittances of leaves were all between 40~50% in near-infrared wave band (800-1100 nm), in which the range of variation was little.

Results and Analysis

The spectral properties of plant leaves are mainly formed by absorption, reflection and transmission of light towards mesophyll cells, chlorophyll, moisture content and other biochemical components in the leaves. In the wave band of 400-700 nm, the spectral properties of leaves were mainly affected by chlorophyll. Due to strong absorption of red light and little absorption of green light by leaf chlorophyll, a wave crest was formed around 550 nm while a wave trough of low transmittances around 680 nm in leaf spectral transmittance. In fast growth period of plants, with the increasing of fertilizer nitrogen, the chlorophyll and nitrogen content also increased so that leaves could absorb more light at 400-700 nm for photosynthesis, therefore spectral transmittance of plant leaves in this wave band decreased with the increasing of fertilizer nitrogen. In the near-infrared wave band (800-1100 nm), the spectral properties of leaves were mainly influenced by the tissue structure in leaves, while the impact of chlorophyll could be negligible. The spectral transmittance of leaf in the near-infrared wave band was 40-50%, which is in line with the results of plant leaf spectrum by other researchers referring to that the absorptivity of leaf was generally less than 10% and reflectivity was about 50% [START_REF] Schepers | Transmittance and Reflectance Measurements of Corn leaves from Plants with Different Nitrogen and Water Supply[END_REF][START_REF] Bauerle | Leaf Absorptance of Photosynthetically Active Radiation in Relation to Chlorophyll Meter Estimates Among Woody Plant Species[END_REF]. The significant negative correlation between spectral transmittance in the waveband of 500-720 nm and leaf chlorophyll content as well as nitrogen content was found in rice and cucumber leaves with the correlation coefficients ranging from -0.8 to -0.9 (P=0.01). Within the waveband of 900-1100 nm, there was not a significant correlation between spectral transmittance and chlorophyll content as well as nitrogen content. Integrated spectral feature parameters, such as difference value and ratio of spectral parameters between feature waveband and reference waveband were adopted in order to minimize the impact of structure and thickness of plant leaves on the spectral data analysis thereby enhancing the accuracy of estimations of chlorophyll and nitrogen content [6, 7] . For instance, spectral feature parameter of log (T940/T650) was used in the portable chlorophyll meter (SPAD-502) produced by Japanese company Minolta to estimate leaf chlorophyll content. A portable meter developed by Fuchigami et al for plant nutrient detection adopts T940/T560 and T940/T720 [START_REF] Fuchigami | Portable Meter to Measure Chlorophyll, Nitrogen and Water and Methods[END_REF]. Based on the correlation between spectral transmittance and chlorophyll content as well as nitrogen content and relevant studies, 560, 650, 720 nm were selected as typical feature wavelengths in the 500-720 nm waveband, and at the same time 940 nm was chosen within the wave band of 900-1100 nm as the reference wavelength to establish 21 spectral feature parameters (Table 2) for nitrogen nutrition diagnosis of plants.

The Correlation between Spectral Transmittance of Leaves and Chlorophyll Contents as well as Nitrogen Contents

The Correlation between Spectral Feature Parameters of Leaves and Chlorophyll Contents as well as Nitrogen Contents

The spectral feature parameters the correlation coefficient between which and chlorophyll content in rice was over 0.94 (P=0.01) were as follows: T940/T560, (T940 -T560) / (T940 + T560), (1/T560 -1/T940) / (1/T940), (T940 -T560) / T940, log (T940/T560), T940/T650, (1/T650 -1/T940) / (1/T940), log (T940/T650). Moreover, the correlation coefficient between all spectral feature parameters mentioned above and nitrogen contents in rice was over 0.91 (P=0.01) (Table 2). The spectral feature parameters the correlation coefficient between which and chlorophyll content in cucumber was over 0.91 (P=0.01) were as follows: (T940 -T560) / (T940 + T560), log (T940/T560), (T940 -T650) / (T940 + T650), log (T940/T650). The correlation coefficient between the two former spectral feature parameters and nitrogen content in cucumber was 0.87 and 0.88 respectively, while that of the latter two and nitrogen content was over 0.90 (P=0.01). It was indicated that the spectral feature parameters of (T940 -T560) / (T940 + T560), log (T940/T560), and log (T940/T650) had a high correlation with chlorophyll and nitrogen contents in cucumber and rice.

3.4

The Experimental Examination of Spectral Feature Parameters Linear regression analysis was carried out of spectral feature parameters, namely (T940 -T560) / (T940 + T560), log (T940/T560) and log (T940/T650), and chlorophyll as well as nitrogen contents in rice and cucumber measured by chemical methods. Chlorophyll and nitrogen contents were estimated through the regression formula and compared with the values measured practically (Table 3). It was found that the relative errors of chlorophyll and nitrogen contents in rice estimated by (T940 -T560) / (T940 + T560), log (T940/T560), and log (T940/T650) were below 5.4%. As regards to chlorophyll contents in cucumber, the relative errors were below 4.5%, and the relative error of its nitrogen content was less than 7.7%. These results indicated that the relative errors of estimated leaf chlorophyll and nitrogen contents with spectral feature parameters of (T940 -T560) / (T940 + T560), log (T940/T560), and log (T940/T650), could meet the requirements of testing the plant nitrogen nutrition in vivo. Thus, they are suitable for use indicators of plant nitrogen nutrition. Among the spectral feature parameters mentioned above, (T940 -T560) / (T940 + T560) was the same as was adopted in the plant nutrition detector developed by [START_REF] Fuchigami | Portable Meter to Measure Chlorophyll, Nitrogen and Water and Methods[END_REF] while log (T940/T650) was the same as used in the portable chlorophyll meter (SPAD-502) produced by Minolta Corporation.

Table 3 Relative error of estimated chlorophyll and nitrogen content using spectral feature parameter (n=30)

Relative error of estimated chlorophyll content (%)

Relative error of estimated nitrogen content (%) Rice Cucumber Rice Cucumber (T940-T560) / (T940 + T560) 5.3±3.

Conclusions

Spectral transmittance in wavelength ranged from 300 to 1100 nm, chlorophyll content and nitrogen content of rice and cucumber leaves treated with culture solution in five different nitrogen levels were measured. According to the correlation analysis between them, 560, 650, and 720 nm as feature wavelengths and 940 nm as reference wavelength were determined for nitrogen detection. Correlation analysis between 21 spectral feature parameters composed by the transmittance at above wavelength, the leaf chlorophyll content and leaf nitrogen content, and combined with their regression examination indicated that spectral feature parameters of (T940 -T560) / (T940 + T560), log (T940/T560) and log (T940/T650) are useful to conduct the plant nutrient diagnosis with less than 8% relative error in rice and cucumber leaves. Therefore, the above spectral feature parameters as plant nitrogen indicators can be used to estimate the chlorophyll content and nitrogen content.

Fig. 2

 2 Fig. 2 Leaf spectral transmittances of cucumber and rice leaves treated with culture solution in different nitrogen levels.

Fig. 3

 3 Fig. 3 Correlation coefficient between leaf spectral (LST), chlorophyll content (CC) and nitrogen content (NC).

Table 1

 1 Nutrient solution in different treatments for cucumber and rice cultivation

		The content of chemical in nutrient solutions (Unit: mg L -1 )
		N0	N50	N100	N150	N200
	Ca(NO 3 ) 2 • 4H 2 O	0	0	826	826	826
	CaCl 2 • 2H 2 O	516	516	0	0	0
	KNO 3	0	607	607	607	607
	KCl	370	0	0	0	0
	KH 2 PO 4	136	0	0	0	0
	NH 4 H 2 PO 4	0	115	115	115	115
	NH 4 NO 3	0	0	0	280	560
	MgSO 4 • 7H 2 O	483	483	483	483	483
		The content of major nutrient elements (Unit: mmol L -1 )
		N0	N50	N100	N150	N200
	N	0	7	14	21	28
	P	1	1	1	1	1
	K	6	6	6	6	6
	Ca	3.5	3.5	3.5	3.5	3.5
	Mg	2	2	2	2	2

Table 2

 2 Correlation coefficient between spectral feature parameters and chlorophyll content as well as nitrogen content

		spectral feature parameter and	spectral feature parameter
		chlorophyll content	and nitrogen content
		rice	cucumber	rice	cucumber
	T940/T560	0.94	0.90	0.93	0.87
	(T940-T560) / (T940 + T560)	0.95	0.91	0.92	0.87
	(T940 -T560) / T940	0.94	0.90	0.91	0.87
	T940 -T560 -T940 × T560	0.92	0.89	0.88	0.86
	(1/T560 -1/T940) / (1/T940)	0.94	0.90	0.93	0.87
	log (T940/T560)	0.95	0.91	0.93	0.88
	log (T940/T560) / log (T940)	-0.92	-0.88	-0.87	-0.84
	T940/T650	0.94	0.89	0.92	0.87
	(T940 -T650) / (T940 + T650)	0.91	0.91	0.89	0.90
	(T940 -T650) / T940	0.90	0.90	0.88	0.89
	T940 -T650 -T940 × T650	0.76	0.78	0.71	0.76
	(1/T650 -1/T940) / (1/T940)	0.94	0.89	0.92	0.87
	log (T940/T650)	0.94	0.92	0.92	0.90
	log (T940/T650) / log (T940)	-0.88	-0.89	-0.84	-0.88
	T940/T720	0.90	0.86	0.88	0.81
	(T940 -T720) / (T940 + T720)	0.90	0.86	0.88	0.81
	(T940 -T720) / T940	0.90	0.86	0.88	0.80
	T940 -T720 -T940 × T720	0.89	0.85	0.87	0.80
	(1/T720 -1/T940) / (1/T940)	0.90	0.86	0.88	0.80
	log (T940/T720)	0.90	0.86	0.89	0.81
	log (T940/T720) / log (T940)	-0.92	-0.84	-0.88	-0.78

Corresponding email: he_dongxian@hotmail.com

Acknowledgments

. This work has been supported by National High Technology Research and Development Program of China (2006AA10A302) and (2006AA10Z206).


Jie Liu

Xiaoyu Li
email: zhoulixiaoyu@mail.hzau.edu.cn

Peiwu Li

Wei Wang

Jun Zhang

Zhu Wei Zhou

Zhu Zhou

Prof Xiaoyu Li

Nondestructive Measurement of Sugar Content in Chestnuts Using Near-infrared Spectroscopy

Keywords: Near-infrared Spectroscopy, sugar, nondestructive measurement, PLS, chestnut

The chestnut (Castanea) is an important fruit in Europe and Asia. As a highly variable fruit, its quality is graded according to nutrition components, especially according to the sugar content, which are traditionally measured by using chemical methods. However, the traditional methods are time-consuming, laborious, and expensive. Here, we analyzed the sugar content of intact and peeled chestnuts by near-infrared spectroscopy. The spectra of intact and peeled chestnut samples were collected in the wavelength range from 833 nm to 2500 nm. The Sample Set Partitioning based on joint X-Y distances was used when the calibration and validation subsets were partitioned. The predictive models for intact and peeled chestnut samples respectively, were developed using partial least squares (PLS) regression based on the original spectra and the spectra derived from different pretreatments. The models developed from the spectra of peeled samples gave accurate predictions. The correlation coefficient (R 2 ) of the optimized model for calibration set and validation set were 0.90 and 0.86. Although the models established on the spectra of intact samples did not perform excellently, they were still qualified to measure sugar content of the chestnut kernel. The correlation coefficient (R 2 ) of optimized model for calibration set and validation set were 0.84 and 0.59. These results suggested that NIR spectroscopy could be used as a fast and accurate alternative method for the nondestructive evaluation of sugar content in chestnuts during orchard and postharvest processes.

Introduction

The chestnut is popular in Asia and Europe for its delicious flavor and abundant nutrition. Every year, more than 1,1Mt tons chestnuts were produced all over the world [1,[START_REF] Ozcimen | A study on the Carbonization of Grapeseed and Chestnut Shell[END_REF] . However, the chestnut is a highly variable species. The nutrition components, flavor and taste are varied according the variety, origin area, cultivation and storage condition [START_REF] Feng | Technique of Chestnut Cultivation China Agricultural[END_REF][START_REF] Pereira-Lorenzo | Rios-Mesa:Chemical Composition of Chestnut Cultivars from Spain[END_REF][START_REF] Freinkel | American Chestnut: The Life, Death, and Rebirth of a Perfect Tree[END_REF] . Therefore, evaluating the nutrition components accurately, especially the sugar content, plays an important role in determination of the price, the time for marketing and preservation, the processing methods and the product quality control [START_REF] Pena-Mendez | Characterization of Various Chestnut Cultivars by Means of Chemometrics Approach[END_REF][START_REF] Gao | The Storage and Process of Chestnuts[END_REF][START_REF] Sacchetti | Stability and Quality of Traditional and Innovative Chestnut Based Products[END_REF] . Recently, the sugar content in chestnuts are determined by using chemical approach, which is destructive, time-consuming, laborious and expensive [START_REF] Pereira-Lorenzo | Rios-Mesa:Chemical Composition of Chestnut Cultivars from Spain[END_REF] . Therefore, developing a rapid nondestructive means to analyze the sugar content of chestnuts, especially for intact chestnuts, is critical to the chestnut industry.

Near-infrared spectroscopy (NIR) is a fast, simple non-destructive, cost-efficient and environment-friendly measurement method that requires minimal sample preparation. It has been successfully used in the quantitative and qualitative analysis of horticultural crops. Because the near-infrared region related to vibration and combination overtones of the fundamental O-H, C-H and N-H bonds, which are the primary functional groups of sugar (glucose, fructose, sucrose, etc), this technology is powerful in quantitative analysis of this component [START_REF] Lu | Morden Near Infrared Spectroscopy Analytical Technology[END_REF][START_REF]Near-Infrared Technology in the Agricultural and Food Industries[END_REF] . Numerous papers have demonstrated the usefulness of NIR in measurement of sugar content of cereal crops, vegetables and fruits [START_REF] Huang | Near Infrared Spectroscopy for on/inline Monitoring of Quality in Foods and Beverages: A Review[END_REF][START_REF] Yan | Basement and Application of Near Infrared Spectroscopy Analysis[END_REF][START_REF] Nicolai | Nondestructive Measurement of Fruit and Vegetable Quality by Means of NIR Spectroscopy: A Review[END_REF][START_REF] Ying | Sugar Content Prediction of Apple using Near-infrared Spectroscopy Treated by Wavelet Transform[END_REF] . Additionally, NIR spectra, with penetration more than 9 mm, can provide adequate information of the core part or the area covered by peel. Tian et al used this technology to predict the firmness of watermelon [START_REF] Tian | Study on Predicting Firmness of Watermelon by Vis/NIR diffuse Transmittance Technique[END_REF] , Han et al applied it to determine the brown heart of pears [START_REF] Han | Nondestructive Detection of Brown core in the Chinese Pear 'Yali' by Transmission Visible-NIR Spectroscopy[END_REF] , and Mexis et al obtained the internal properties in packages of walnuts by this means [START_REF] Mexis | Effect of Packaging and Storage Conditions on Quality of Shelled Walnuts[END_REF] . Recent research also showed that the NIR was employed for detection of moldy chestnuts [START_REF] Liu | Study of Discrimination of Moldy Chestnut Using Near-Infrared Spectroscopy[END_REF] and for quantitative analysis of targets with peel such as oranges [START_REF] Tewari | Determination of Origin and Sugars of Citrus Fruits using Genetic Algorithm, Correspondence Analysis and Partial Least Square Combined with Fiber Optic NIR Spectroscopy[END_REF] , avocados [START_REF] Blakey | Influence of Water and ABA Supply on the Ripening Pattern of Avocado (Persea americana Mill.) Fruit and the Prediction of Water Content using Near Infrared Spectroscopy[END_REF] , peanuts [START_REF] Sundaram | Application of NIR Reflectance Spectroscopy on Determination of Moisture Content of Peanuts: A Non Destructive Analysis Method[END_REF] and intact asparaguses [START_REF] Flores-Rojas | Quantitative Assessment of Intact Green Asparagus Quality by Near Infrared Spectroscopy[END_REF] . However, although it is beneficial and meaningful to apply NIR spectroscopy to the nut industry, NIR has not been popularly used for evaluating the quality of nuts, such as chestnuts, walnuts, and hazelnuts. In this investigation, we exploited the feasibility of NIR spectroscopy for evaluating the sugar content of intact and peeled chestnuts to provide an effective method for the chestnut industry. Also, the efficiency and influence of different preprocessing methods on calibration models were compared to identify the optimized model, as well as to provide reference for further research.

Materials and methods

Fruit samples

185 chestnut smaples, from MaCheng area (Hubei, China) were used in this study, and the individual weight of intact chestnuts ranged from 9.26 g to 27.74 g. Considering the chestnut growing property, the samples were representative enough for different growing conditions, maturation and weather at harvest time. The samples were stored under standard refrigeration conditions (0 °C~4 °C, 80%~90% relative humidity, SB/T 10192-1993) for three to four months before experiment tests. The samples were exposed to temperature 26 °C for 2 hours before experimental tests to allow for temperature equilibrium. The experiment was executed in 10 days with same time interval in order to provide models with wide applicability as the change process of chestnut fruit during storage is perplexing.

Spectra collection and reference value determination

NIR spectra were acquired by using a VECTOR33 NIR spectrometer (Brucker Optics, Ettlingen, Germany) equipped with fiber optic accessories and were stored on a personal computer connected via an AQP card to the spectrometer. The instrument control, parameter setup, spectral acquisition and storage were carried out with the OPUS software (v. 4.0 Brucker Optics, Ettlingen, Germany). The environment temperature was kept at 26 °C and each sample was scanned 64 times, from which an average spectrum was calculated. Spectra were collected in reflectance mode in the range of 833 nm~2500 nm at 1.25×106 nm and stored as the absorbance mode. To stabilize the light sources, the spectrometer was warmed up for a period of 1 hour prior to measurement. Before the sample measurement, the spectrum of standard background was obtained as the basic reference. Firstly, every sample was scanned with an intact peel. To avoid differences caused by optical path and angle of incidence, the chestnut samples were scanned with their flat side facing the probe and covering the probe completely. Secondly, it was dissected parallel to the flat side and the hemispheric part was set into the sample container to be scanned. For the same reason, the probe was completely covered by the section.

After spectra acquisition, the kernel of the sample was taken out for reference value determination according to the Chinese national standard methods. The sugar content was determined by using an enzymatic method and expressed as the percentage of the fresh weight (GB T 5009.7-2003, SAC).

Data analysis

The sample set partitioning based on joint X-Y distances (SPXY) was performed to partition the calibration and validation subsets at the rate of 3 to 1. Because of its con-sideration of the variability in both x and y information, the SPXY was cited as a method that can cover the multidimensional space more effectively in comparison with the randomly sampling (RS) or partitioning schemes based only on x-or y-space alone (such as the Kennard-Stone (KS) algorithm or concentration gradient sampling) [START_REF] Galvao | A Method for Calibration and Validation Subset Partitioning[END_REF] . Consequently, this method can be potentially used to improve the performance of the predictive models.

Before calibrating the models, four preprocessing methods, namely multiplicative scatter correction (MSC), standard normal variate transformation (SNV), first derivation (FD) and second derivation (SD) were applied to the spectra. These methods can effectively reduce or remove shifts of baseline and superposed peaks, or interferences due to scatter, particle size, and light distance changes [START_REF]Near-Infrared Technology in the Agricultural and Food Industries[END_REF][START_REF] Isaksson | The Effect of Multiplicative Scatter Correction (Msc) and Linearity Improvement in Nir Spectroscopy[END_REF] . However, they also result in amplification of noises or loss of information. Therefore, the contribution of a preprocessing method to the predictive models depends on the characteristics of the spectra.

The partial least square (PLS) regression method was used to establish the predictive models for sugar content. The optimal latent variables were determined by the lowest root mean square error of cross validation (RMSECV) among the calibration set and the models were validated with the independent validation set. The performance of models were evaluated by the correlation coefficient (R 2 ) between predicted values and reference values of the parameters, the root mean square error of calibration (RMSEC) and the root mean and square error of prediction (RMSEP). The higher the R 2 is, the better the model performed, while the lower the RMSEC and RMSEP are, the better the model is. All the data analyses were carried out with the aid of Matlab software (V.7.6, Mathworks, Natick, USA) and the PLS models were developed by using the PLS toolbox (V. 5.20, Eigenvector Research, Inc., Wenatchee, USA).

Result and discussion

Spectra configuration, reference values, and subsets partition

Raw spectra of intact and peeled samples are presented in figure 1. It was found that the absorbance spectra of intact samples were scatter over a wide range, while those of the peeled samples fell in a narrow range. The more prominent diversity of peel surface possibly contributed to these differences. However, the two kinds of spectra show similar features over the entire spectra range except for the bands between 833 nm~1053 nm and 2000 nm~2040 nm. The former band mainly reflected the information of color or texture about the sample surface and the later band possibly revealed the features of peel component. Generally, from both the spectra of intact samples and the spectra of peeled samples, characteristic absorption bands associated with sugar can be observed. For example, the dominant peak at around 1450 nm was influenced by the first -OH overtone. The weak bands at around 1780 nm were mainly attributed to the first sugar-related overtone and the prominent peak around 1160 nm was also associated with sugar [START_REF] Osborne | Food and Beverage Analysis[END_REF][START_REF] Walsh | Sorting of Fruit using Near Infrared Spectroscopy: Application to a Range of Fruit and Vegetables for Soluble Solids and Dry Matter Content[END_REF] . Figure 2 shows the raw spectra of a randomly selected sample with and without peel. The difference of reflectance between the two kinds of spectra is obvious, indicating the influence of the peel on the spectrum. The details of the reference data were shown in Table 1. The sugar content of samples was scattered from 6.6% to 14.6%. It is a wide range since approximately 10 % of the fresh weight is sugar normally. According to the SPXY algorithm, three quarters of the samples were chosen to be the calibration set while the rest served as independent validation. From the obvious difference between the spectra acquired in intact and peeled condition, even of a same sample, it is clear that the subset structure for intact and that for peeled chestnuts are different. The statistic descriptions of subsets were also summarized in Table 1.

Calibration and validation of prediction models for intact sample

In the entire spectral range, the prediction models based on the raw and pretreated spectra of intact samples were established by PLS regression, respectively. Independent validation sets were used to evaluate the performance of models. Table 2 presents detailed information about the models. It can be found that, the FD model had the highest R 2 value of calibration and validation and the lowest RMSEC and RMSEP (0.84, 0.59, 1.12 and 1.32, respectively). Although the SD model had great parameters for calibration, it appeared as an over-fitting one because of the poor result for validation, which indicated the effect of derivation preprocessing in extracting the useful information in spectra and the result of this method to noise amplification along with the order increasing. The SNV was not a good preprocessing method for intact samples since the model performed worse than the one based on the raw spectra. While the MSC model had same parameters with the model for peeled samples, its latent variables was 9.

Calibration and validation of prediction models for peeled sample

The PLS models for peeled samples were calculated in the same way and the results were presented in Table 3. Similar with the results of models for peeled samples, the FD model gave the best performance with 0.91, 0.87, 0.76 and 0.74 as the R 2 of calibration, R 2 of validation, RMSEC and RMSEP, respectively. The SD model would also be over-fitting because of the unacceptable difference of R 2 of calibration and R 2 of validation while it had less factor number. Compared with the model based on raw spectra, the MSC and SNV models shown their capability of obtain the relevant in-formation in spectra. However, these two models were not reliable enough because the differences between the R 2 of calibration and R 2 of validation were unacceptable. It can be seen that, the models based on the spectra of peeled samples performed better than those based on the spectra of intact samples. The influence of peel in spectra acquisition might be the reason. However, their spectral characteristics were similar because the effects of different preprocessing methods followed the same order: the first deviation was the most powerful; the second deviation was the least effective, and the SNV and the MSC performed medium and their results were near to that of the non-preprocessing. The poor performances of MSC and SNV models both for intact and peeled samples shown that the influence of peel to predictive models came more from its obstruction to penetration instead of its surface diversity.

According to Shenk and Westerhaus [START_REF] Shenk | Calibration the ISI Way[END_REF] , an R 2 value of more than 0.9 indicates excellent quantitative information, a value of between 0.70 and 0.89 means good quantitative information, and a value of between 0.50 and 0.69 is qualified for good separation of samples into high, medium and low groups. Therefore, the FD models based on the spectra of samples peeled and intact, were reliable to test the sugar content of chestnuts.

Conclusion

The present work addressed the assessment of chestnut quality by near-infrared spectroscopy. The presented results demonstrated the potential of NIR spectroscopy in evaluating the sugar content of intact and peeled chestnuts. The correlation coefficients of the optimized models for sugar were 0.90 for peeled samples and 0.83 for intact samples. These results proved that NIR can be used as a nondestructive and rapid means for determining the sugar content of chestnuts with reduced time and labor compared with traditional methods. Further work would be needed to develop more accurate and robust models by selecting sensitive bands, intensifying spectra energy or combining chemometrics methods. Portable or online devices could be developed based on these results to offer information for decisions on harvest, market or process, which will benefit the whole chestnut industry.

Fig. 1 .Fig. 2 .

 12 Fig. 1. Raw spectra of chestnut samples (Left: the spectra of intact samples; right: the spectra of peeled samples.)

Table 1 .

 1 Statistic descriptions of subsets partition and reference sugar content (SN: sample number; SD: standard deviation; Max: maximum; Min: minimum.)

	Subset	SN	Mean	SD	Max	Min
	Intact Calibration	139	9.96	1.79	14.55	6.57
	Intact Validation	46	9.32	1.38	12.92	6.58
	Peeled Calibration	139	9.86	1.82	14.55	6.57
	Peeled Validation	46	9.60	1.37	13.37	6.62
	Total	185	9.80	1.72	14.55	6.57

Table 2 .

 2 Statistics parameters of PLS models for sugar content in intact samples (NP: no preprocessing; MSC: multiplicative scatter correction; SNV: standard normal variate transformation; FD: first derivation; SD: second derivation; R 2 : correlation coefficient; RMSEC: root mean square error of calibration; RMSEP: root mean square error of prediction).

	Preprocessing method	Calibration R 2 RMSEC R 2 Validation	RMSEP	Factors
	NP	0.676	1.287	0.583	1.531	8
	MSC	0.773	1.107	0.597	1.330	9
	SNV	0.669	1.300	0.554	1.400	7
	FD	0.838	1.116	0.589	1.320	4
	SD	0.967	0.445	0.516	1.383	4

Table 3 .

 3 Statistics

	parameters of PLS models for sugar content in peeled samples (NP: no
	preprocessing; MSC: multiplicative scatter correction; SNV: standard normal variate
	transformation; FD: first derivation; SD: second derivation; R 2 : correlation coefficient;
	RMSEC: root mean square error of calibration; RMSEP: root mean square error of prediction.)
	Preprocessing method	Calibration R 2 RMSEC R 2 Validation	RMSEP	Factors
	NP	0.505	1.558	0.625	1.112	7
	MSC	0.773	1.107	0.597	1.330	3
	SNV	0.699	1.278	0.809	0.810	6
	FD	0.907	0.761	0.865	0.739	4
	SD	0.802	1.084	0.653	1.097	2

Acknowledgement

The authors really appreciate the financial support provided by Foundation of Ph.D Site in University (20090146110018) and China Scholarship Council (2008104710). And, we gratefully acknowledges for Prof. Li Peiwu's guidance and the help of Liu Pei during the spectra acquisition.

1 This work is supported by Foundation of Ph.D Site in University (20090146110018).


Qingsong Cao

Jie Zhu

Zhifan Gao

Guoliang Xiong

{qingsong Cao

Design of Integrated Error Compensation System for the Portable Flexible CMMs

Keywords: flexible arm, coordinates measuring machine, sensor, strain, temperature, error compensation

The ideal mathematical modle and error model of a 6-DOF portable flexible coordinate measuring machine (CMM) was established. A parameter data acquisition system for the portable flexible CMM was designed in this paper, which consisted angle sensors, temperature sensors, strain transducers and microcontroller, they were set in each joint and arm. The microcontroller was used to deal with the sensors' data and send them to PC. The compensation dosages are feedbacked to the measuring program to keep these errors compensated continually. The error compensation program was developed by LabVIEW , was used to realize the work of communication with microcontroller and data processing. It shows that the integrated error compensation system can work well.

Introduction

The traditional orthogonal coordinate measuring machines have the advantage of simple kinematical model and high measuring accuracy. However, with the development of the modernization of industry and manufacture, the limitations of traditional perpendicular CMMs also increasingly exposed to us, which can not meet the application requirements in many cases.

The portable flexible CMMs [START_REF] Faro Technologies Inc | FaroArm offers flexible measurement solution[END_REF] connect arm through revolting joint by turn, which is a new type of multiple degree and non-Cartesian system. It has the advantage of smaller volume, lighter weight, larger measuring range, portable and barrier-free measure compared with the traditional orthogonal CMMs. It is particularly suitable for the on-site mobile measure and it has gradually become the developing trend of the measurement field. However, due to its totally new structure, the mathematical modeling procedure is no longer simple. On the other hand, the error of each joint will be seriously magnified by the measuring arm, and it will seriously affect the measurement accuracy. Therefore detecting and compensating for this error is very necessary.

For portable flexible CMMs, the rotation angle of each joint, along with the mechanical parameters including length of each measuring arms and offset of every two joints etc, are substituted the kinematical model built by D-H coordinate transform method. Then the measurement of 3-D coordinates of the probe can be get. Therefore, the precision of 3-D coordinates is directly influenced by the angle error of each joint and the length error of every mechanical arm. To ensure the measuring accuracy, error compensation system must be established.

Ye et al. [START_REF] Huang | Error modeling for multi-joint coordinate measuring machine[END_REF][START_REF]Modeling and error analysis for Human-simulating multi-joint coordinate measuring machine[END_REF] considered error of the structural parameters during the machining and assembling process as the largest error source, and correct ideal model of the CMMs to get its error model. In this way, the measurement accuracy is greatly improved. Wang et al. [START_REF] Liu | Mathematical Model and error Analysis of the Articulated Arm Flexible CMM[END_REF] analyzed various factors that affect measurement results in detail, and consider that the processing and assemble error, the environmental temperature and strain, etc. affect the measurement precision of the CMMs. Wang [START_REF] Xu | The develop of portable Articulated Arm CMM[END_REF] analyzed the impacts on the measuring results caused by external environment such as temperature, humidity and external power, and presented several measures that should be taken to reduce them.

2

The Principle of Compensation for Integrated Error

Mathematical Model of the CMM

Portable CMM adopts the whole serial revolted-joint structure, the relative position and posture between every two rods is the basis of coordinate measuring. And coordinate system of each rod should be built for the analysis of this relationship between them [START_REF] Zhou | Mathematical Model and Parameter Calibration of the multi-joint coordinate measuring machine[END_REF]. In 1955 Denavit and Hartenberg put forward the famous D-H homogeneous transformation method. By this method, 6 DOF coordinate system for structure schematic diagram of the CMM is built, as shown in Fig. 1. The coordinate transformation matrix of coordinates from number i-1 to number i is signed asT i i-1 [START_REF] Denavit | A kinematic notation for lower pair mechanism based on matrices[END_REF]. A CMM with n joints, the coordinate transformation matrixes of coordinate system are as below

T n n-1 ,T n-1 n-2 ,…,T 2 1 , T 1 0 .
If the position of one point in the final coordinate system is known, then coordinates of the measuring head in the base coordinate system can be obtained through continuous transformation of coordinate systems. On the basis of the coordinate systems built above and D-H coordinate transformation principle, the coordinate transformation matrix of the measuring head with respect to the base coordinate system can be expressed as 0 1 2 3 4 5 6 1 2 3 4 5 6 7 1

1 0 0 0

0 1 0 0 0 0 1 0 0 0 1 A T T T T T T T L ⎡ ⎤ ⎢ ⎥ ⎢ ⎥ = = ⎢ ⎥ ⎢ ⎥ ⎣ ⎦ 1 1 3 1 2 2 4 2 1 1 3 1 2 2 4 2 2
L L L L L L θ θ θ ⎡ ⎤ ⎡ ⎢ ⎥ ⎢ - - ⎢ ⎥ ⎢ × × ⎢ ⎥ ⎢ ⎢ ⎥ ⎢ ⎣ ⎦ ⎣ - - ⎡ ⎤ ⎢ ⎥ - ⎢ ⎥ × × ⎢ ⎥ ⎢ ⎥ ⎣ ⎦
0 0 0 1 0 0 0 1 L L s i L L L L θ ⎤ ⎥ ⎥ ⎥ θ θ θ ⎡ ⎤ ⎢ ⎥ - ⎢ ⎥ ⎢ ⎥ - ⎢ ⎥ ⎣ ⎦ - ⎡ ⎤ ⎡ ⎢ ⎥ ⎢ ⎢ ⎥ ⎢ × × ⎢ ⎥ ⎢ ⎢ ⎥ ⎢ ⎣ ⎦ ⎣ ⎤ ⎥ ⎥ ⎥ ⎥ ⎦ (1)
As can be deduced from this mathematical model, the measurement result depend on the arms length Li and the joint's rotating angle θi , the error of the arms length and the joint's rotating angle affected measuring accuracy directly.

Principle of compensation for temperature error

During the measurement procedure, the environmental temperature should be kept as constant as possible. If not, the deviation of the temperature from the reference of 20 will result in difference between real length and ideal length of each ℃ arm, which should not be neglected. For this kind of difference, temperature element is sticked to the arm to monitor its temperature, and then get it processed to get the relevant compensation dosage. The formula of the compensation dosage is ( )

20 i i L T α = - × . ( 2

) Δ

In this formula, T is the environmental temperature, α i is thermal expansion coefficient of the material. So when the affection of thermal variation on the measuring results is considered, and the compensation dosage is added to the ideal mathematical model, we can get the mathematical model with errors of temperature variation as follow, ' 0 1 2 3 4 5 6 1 2 3 4 5 6 7 1 1

1 1 3 3 1 2 2 4 4 2 1 1 3 3 1 2 2 4 4 2 2 2
1 0 0 0

0 1 0 0 0 0 1 0 0 0 1 cos 0 sin ( )cos cos 0 sin ( )cos sin 1 cos ( )sin sin 0 cos ( )sin 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 A T T T T T T T L L L L L L L L L θ θ θ ⎡ ⎤ ⎢ ⎥ ⎢ ⎥ = = ⎢ ⎥ + Δ ⎢ ⎥ ⎣ ⎦ + Δ + Δ ⎡ ⎤ ⎡ ⎢ ⎥ ⎢ - + Δ - + Δ ⎢ ⎥ ⎢ × × ⎢ ⎥ ⎢ + Δ ⎢ ⎥ ⎢ ⎣ ⎦ ⎣
L L L L L L L L L L L L L L ⎤ ⎥ θ θ θ ⎥ ⎥ ⎥ + Δ - - + Δ ⎡ ⎤ ⎡ ⎢ ⎥ ⎢ - + Δ - + Δ ⎢ ⎥ ⎢ × × ⎢ ⎥ ⎢ + Δ - ⎢ ⎥ ⎢ ⎣ ⎦ ⎣ - + Δ + Δ ×
0 0 0 1 0 0 0 1 si L L L L L L θ θ θ θ θ θ + Δ ⎡ ⎤ ⎡ ⎢ ⎥ ⎢ + Δ ⎢ ⎥ ⎢ × ⎢ ⎥ ⎢ + Δ ⎢ ⎥ ⎢ ⎣ ⎦ ⎣ θ ⎤ ⎥ ⎥ ⎥ ⎥ ⎦ ⎤ ⎥ ⎥ ⎥ ⎥ ⎦ (3)

Principle of compensation for strain error

In the measurement procedure, the bending deformation on measurement arm will result in the extra rotating angle of each joint. As the angle deviation would be seriously magnified through the arm, the impact caused must not be neglected.

The bending deformation caused by the operation force can be detected by strain gages sticked symmetrically around the measuring arm. The bridge type is most frequently used, which can amplify signal effectively. The circuit diagram for strain measurement is shown in fig. 2.

Fig.2. Block diagram of strain-measuring circuit

As is shown in the figure, the bridge is in state of equilibrium, and there are no signals output when the measuring arm is not forced. Two strains are sticked on the adjacent bridge arms. The resistance of strain A gets bigger and that of strain B decreases when the arm is exposed to stress, the bridge is no longer balanced, and there is voltage signal output in this situation. As resistance strain is used in this paper, this signal is very weak, and it must be amplified first so that it can be treated afterwards. According to the above shows that can block, amplified output voltage of bridge road can be expressed as

0 0 2 K K E Li U Li ⋅ ⋅ ⋅Δ = ⋅ (4)
Where, E is the input voltage, K is the sensitivity coefficient of the strain gauge selected, Li is the length of the measuring arm. So the length variation of measuring arm can be obtained and expressed as

0 0 2 U Li Li E K K ⋅ ⋅ Δ = ⋅ ⋅ (5)
And the corresponding extra rotation angle of the joint is

1 1 0 0 0 cos cos 2 Li E K K Li i Li Li Li U Li E K K θ - -⎛ ⎞ ⋅ ⋅ ⋅ ⎛ ⎞ Δ = = ⎜ ⎟ ⎜ ⎟ + Δ ⋅ ⋅ + ⋅ ⋅ ⋅ ⎝ ⎠ ⎝ ⎠ (6)
Namely, this △θi is the extra rotation angle of each joint caused by the measuring arm's own gravity or external operation force. And it is used as the compensation value and is substitute to mathematical model of the CMMs. So mathematical model with strain error can be expressed as 0 1 2 3 4 5 6 1 2 3 4 5 6 7 1

1 1 1 1 3 1 1 2 2 2 2 4 2 2 1 1 1 1 3 1 1 2 2 1 0 0 0 0 1 0 0 '' 0 0 1 0 0 0 1 cos( ) 0 sin( ) cos( ) cos( ) 0 sin( ) cos( ) sin( ) 1 cos( ) sin( ) sin( 0 0 0 0 0 0 1 A T T T T T T T L θ θ θ ⎡ ⎤ ⎢ ⎥ - +Δ +Δ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎣ ⎦ + Δ + Δ + Δ - + Δ - + Δ + Δ ⎡ ⎤ ⎢ ⎥ + Δ - + Δ + Δ + Δ - ⎢ ⎥ × × ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎣ ⎦ 4
0 1 0 0 0 0 0 1 sin( ) 0 cos( ) cos( ) cos( ) 0 n( ) sin( ) cos( ) 0 sin( ) sin( ) sin( ) 0 cos( 0 1 0 0 0 0 1 θ θ θ ⎡ ⎤ ⎢ ⎥ + Δ + Δ ⎢ ⎥ ⎢ ⎥ - ⎢ ⎥ ⎢ ⎥ ⎣ ⎦ - +Δ +Δ +Δ +Δ +Δ +Δ ⎡ ⎤ ⎢ ⎥ + Δ + Δ + Δ + Δ + ⎢ ⎥ × × ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎣ ⎦ 6 1 0 6 6 ) c o s ( ) 0 1 0 0 0 0 0 1 L θ θ θ ⎡ ⎤ ⎢ ⎥ Δ + Δ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎣ ⎦ (7)
3 Design of the compensation system for integrated errors

Overall Scheme

To achieve compensation for errors caused by thermal change and strain, a compensation system is designed in this paper, which consists of the acquisition of temperature and strain, the calculation of compensation dosage and the modification of corresponding mathematical model. The overall diagram is shown in Fig. 3. Therefore, measures have to be taken to modify these errors.

A new single-wire enhanced digital thermometer DS18B20 [START_REF]Application of Single Chip Processor and C language programming[END_REF], which is produced by the DALLAS Corporation, is used in this paper. The measuring rang of temperature of this sensor is -55 ~ 125℃,and the measurement accuracy is ± 0.5 ℃ among -10~ 85 . As the sensor ℃ adopts a single-wire structure, the information can be sent or output by a single-wire interface, and the PC can read、write and convert the temperature through an I/O line. In addition, the sensor can achieve multiple points of temperature measurement, thus it can reduce the amount of line for temperature measurement [START_REF]Application of Single Chip Processor and C language programming[END_REF]. The temperature measurement circuit is shown in fig. 4. The temperature obtained from the single-wire digital thermometer DS18B20 is sent to computer through serial communication, and then it is compared with the ideal temperature of 20℃to get the corresponding length change, using the thermal expansion coefficient of the selected material. On the PC, LabVIEW is used to obtain and calculate the compensation dosage of the corresponding temperature through serial communication. The program diagram is shown in fig. 6.

Design of the Error Compensation System of Strain

The arm of flexible CMM usually adopts long and thin circular pipe, so it is easy to bend and deform under its own gravity and the exterior operation power. Previous studies showed that it creates bending deformation of more than 10″at the two ends of the arm is very common. This is one of the most important factors limiting the measuring accuracy of this kind of CMMs.

The bending and deformation of CMM's each arm is not only in relation to the structural parameters of the arm, but also to the extension of the arm. It is difficult to calculate the amount of the bending and deformation of the arm precisely by theoretical calculation. So it is necessary to paste numbers of strain gauges to monitor deformation on each arm, and then make corresponding program to compensate for the errors immediately.

The bending and deformation of measuring arm is equal to increase the rotation angle of each joint additionally. Strain gauges are pasted symmetrically on the arm to monitor the bending and deformation along the axis in this paper. The strain gauges are linked as form of half bridge, and circuit of strain measurement is shown in fig. 7. The programs developed with LabVIEW on the PC for strain measurement is just like that of the temperature, we can replace the formula with the formulae 6 above. Take into account fully, the error of environmental thermal variation and dynamic strain was compiled with LabVIEW thus the Complete error compensation system was established. It is also the key technology to improve the measurement accuracy.

Conclusion

According to the D-H method, the ideal mathematical model of the portable CMM was established in this paper; on the bases of the ideal mathematical model, the mathematical model with error was established on the basis of analysis of parts of the error sources. Sensors and microcontroller were applied in parameter data acquisition system, which realize the parameters measurement of temperature and strain. The successful development of data acquisition system is key technologies for high precision calibration and error compensation system, which can improve the measurement accuracy of portable flexible CMM effectively.

Fig. 1 .

 1 Fig. 1. Coordinates of the portable CMM

Fig. 3 .

 3 Fig.3. Overall diagram of the error compensation system

Fig. 4 .Fig. 5 .

 45 Fig.4. Measurement circuit of temperature The measuring circuit of thermal variation is very simple, and its function is mainly accomplished by software. The program diagram is shown in fig.5.

Fig. 6 .

 6 Fig.6. Program diagram of thermal error compensation

Fig. 7 .

 7 Fig.7. Circuit of strain measurement

Fig. 8 .

 8 Fig.8. Circuit of strain measurement

Acknowledgements

This work is supported by Science Foundation of Jiangxi Province Educational Committee (GJJ10445).


Zhongqi Li

Fengping Yang

Qirong Xing

Design of Multifunction Vehicle Bus Controller

Keywords: TCN, MVBC, FPGA

The Train Communication Network (TCN) is widely used in high-speed train. However, its core technology -Multifunction Vehicle Bus Controller (MVBC) is owned by foreign companies, which limits the development of high-speed railway in China. Based on the principle of Multifunction Vehicle Bus(MVB), and analysis of real-time protocol and functions of bus controller to be achieved, this paper describes the design of MVBC which is divided into seven modules, Encoder, Decoder, Telegram Analysis Unit(TAU), Configuration Memory, Traffic Memory Controller(TMC), Arbitrator and Microprocessor Control Unit(MCU). And then it designs each module with Very-High-Speed Integrated Circuit HardwareDescription Language(VHDL) in the integrated development environment of Quartus Ⅱ developed by Altera Company. A new MVBC is developed with Field Programmable Gate Array(FPGA). The test shows that it can take place of MVBC Application Specific Integrated Circuits (ASIC).

Introduction

The TCN bus was standardized in the Plenary Session of the IEC Technical Committee 9, Kyoto, Japan, October1999, which resulted to the IEC 61375 Standard. The IEEE Vehicular Society also standardized the TCN bus, which resulted to the IEEE 1473-T Standard. The main goal of the TCN is to set a single train equipment specification that is compliant with the requirements/needs of different railway service providers and to warrant the interconnection of any number of different vehicles/units from possibly different manufacturers in the same train. MVB is one component of TCN. The MVB is the Vehicle Bus specified to connect standard equipment, located in the same vehicle, or in different vehicles in the TCN. The MVB can address up to 4095 devices, of which 256 are stations capable of participating in message communication. In order to allow these devices to communicate with each other, a common communication interface card is designed which is independent of the chosen physical layer and functions associated with each device. The MVBC is an ASIC chip which realizes the physical and major parts of the link layer protocol of the MVB.

System Description and Architecture

The MVB Controller contains the encoders/decoders and the logic to control the Traffic Memory, which decodes the incoming frames and addresses the Traffic Memory accordingly. This paper describes the design of MVBC which is divided into seven modules that is Encoder, Decoder, Telegram Analysis Unit(TAU), Configuration Memory, Traffic Memory Controller(TMC), Arbitrator and Microprocessor Control Unit(MCU). Fig. 1 shows MVBC architecture.

Fig. 1. MVBC architecture

Implementation steps of the controller function as follows, chip power, reset, configure the device address, models and other services, start work. When sending data, MCU reading the corresponding data from Traffic Memory(TM). write to send buffer, then MCU send instructions of send data to the encoder. When accepting data, the Manchester decoder receiving signals from the bus controller, upon decoding, verification and data is written in receive buffer, and notifies the main control unit. MCU read data from the receiving buffer, writes corresponding area of TM. MVBC communicate with the microprocessor through TM which uses dual-port RAM.

Encoder

Encoder engages in Manchester encoding of Master_Frame and Slave_Frame, and generation and sending CRC check sequence. Above-mentioned work is controlled by MCU. The specific tasks of the module are as follows: [START_REF]Electric Railway Equipment-Train bus-Part1: Train Communication Network, International Electro technical Commission (IEC)[END_REF] The parallel data are extracted from the buffer, (2) The parallel input data transform into serial data suitable for MVB, (3) Add to delimiter for ready to send data, (4) According to the length of ready to send data, add one or more 8-bit CRC check_sequence. ( 5)Manchester encode for data to be send in order to improve communication quality.

Structure of encoder according to the above design of the task is as follows, Digital control unit, Parallel Convert Serial Unit, Frame Delimiter Unit, FIFO unit, Multi-selector unit, CRC Generation Unit, Manchester encode unit. Fig. 2 shows Encoder architecture.

Decoder

The main functions of the decoding module include Manchester decoding and error detection of received data, then decoded without error serial data convert to parallel data which stored in the buffer zone and supply of top applications. Decode is the anti-process of encode which will have decoded data encoded, it is also the original appearance of data. The task of error detection mainly includes the error detection of CRC check code, error of frame length and error of Manchester skipping, etc. Decoder includes decoding control unit, the detection of start bit unit, delimiter detection unit, Manchester decode unit, Serial Convert Parallel Unit, FIFO unit, CRC check unit and the length check unit. Fig. 3 shows Decoder architecture. Internal access mode use a dedicated signal line to achieve an internal read-write access in which read access is through the direct connection method to achieve fast access, while the form of written interview by paragraph to visit it to each configuration register which is divided into paragraphs (up to 16), identify by paragraph address and data. So long as we know the corresponding data segment address and the segment can be achieved within the configuration register access.

In the external access mode, then use a common bus address to visit. As the external access is targeted TM or CPU, on the TM and the CPU access is controlled by TMC and ARB.

Telegram Analysis Unit

TAU is mainly responsible for the analysis of telegram. The received packet is passed to the control unit results MCU, then according to the feedback control unit to the corresponding treatment. There are eight kinds of situations. TAU is composed of timer and state machine. Fig. 4 shows TAU architecture.

Fig .4. TAU architecture

State machine is responsible for the analysis of the telegram and the corresponding control. Timer with the state machines perform a variety of timing tasks, while ensure the successful completion of state machines to switch among various states and the corresponding operation.

Traffic Memory Controller

TMC is composed of sink time manager、master state machine、word access state machine and selector. Master state machine controls all of TM and generates control sequences. Each word can be accessed by controlling word access state machine. When word being accessed completely, the word will automatically add an access counter, up access to all word. Word access state machine is formed by the five state, IDLE STATE, ACC_BEGIN STATE, WAIT STATE, W_TMRDY STATE and DATA_VALID STATE. Transition among states is shown in Fig. 5 shows transition among states.

Arbitrator

Arbitrator is used to solve the conflict when CPU and MVBC access TM at the same time, arbitrator at the same time allows only one access TM.

Microprocessor Control Unit

MCU is a core part of MVBC almost control all the other modules work, play a role in overall control. MCU is not directly involved in concrete action, but by controlling the other modules to complete the appropriate action treatment. MCU is composed of the distribution of Master frame, port pretreatment, port post-processing, Transport Data Unit, command processing unit. Fig. 6 shows MCU architecture. Frame of process data simulated in Quartus Ⅱ. In Fig. 7, F_code is 0000 which means 16 bit process data. The corresponding response frame is 16-bit. The last 12 bit of master frame is Logical address of process data. Data_out is main frame of process data which includes a frame header, frame end, and CRC checksum. Send_end is high that means sending master frame of process data is over. The delimiter and checkout value is successfully added. The simulation result of Salve Frame is shown in Fig. 8.

Conclusions

The paper proposes a novel MVB design method using SOPC technology. The proposed MVB controller was successfully tested at a mixed MVB environment. It combines CPU and MVB controller into one single FPGA, so this method can save PCB space and reduce total power consumption. The availability of multi million-gate FPGA devices and SOPC technology has opened the possibility to create complex single chip systems and can accelerate system performance remarkably.

With the SOPC technology, we have created a novel design and implementation method of the MVB controller. This method can great reduce the chip numbers and card size and power consumption relative to the traditional method. And the design is based on a soft IP core, so it can be easily integrated into other applications.

Fig. 2 .

 2 Fig. 2. Encoder architecture Digital control unit plays a controlling role of the operation of the other. First Input First Output(FIFO) unit is the input data buffer. Frame delimiter unit is the role as follows; add to Start_Bit for frame, Start Delimiter of Master_Frame, Start Delimiter of Slave_Frame. Parallel Convert Serial Unit read data from buffer and convert parallel data to the serial data. Manchester encode unit encodes data on the communication. Outgoing data CRC check sequence generated by CRC generation unit. When the data output, Multi-selector unit selects the starting delimiter, outgoing data and the CRC check code.

Fig. 3 .

 3 Fig.3. Decoder architecture

Fig. 5 .

 5 Fig.5. Transition among states

Fig. 6 .

 6 Fig.6. MCU architecture

Fig. 7 .Fig. 8 .

 78 Fig.7. Simulation of Master Frame of process data

Fig. 9 .

 9 Fig.9. Simulation of Master Frame of message data

Acknowledgement

The work described in this paper has been supported by Foundation of JiangXi Educational Committee (GJJ09203).


薛龙 Long
email: :ultimata@163.com

Application of IDL and ENVI redevelopment in hyperspectral image preprocessing

Keywords: ENVI, redevelopment, hyperspectral image

In the last few years, hyperspectral imaging technique has had a bright future for application on nondestructive detection of agricultural products. However, in experiment, there are usually dozens or even hundreds of hyperspectral images. It needs to spend lots of time in preprocessing all the hyperspectral data. Software ENVI is a kind of image processing software, although the users can define their own algorithms and apply to the opened bands in ENVI or the bands and spectra operation of the images, it doesn't provide program to batch process. In this paper, the custom function "normalized" of Interactive Data Language (IDL) is applied to achieve batch processing on multiple hyperspectral images.

Introduction

Software ENVI (The Environment for Visualizing Images) is image processing software. It is developed by the sciences of remote sensing using Interactive Data Language (IDL). It combines the latest spectral image processing and image analysis technology with an intuitive, user-friendly interface to help you get meaningful information from imagery. It can fast, conveniently, correctly extract information from geography and special image data. Therefore, it has been widely used in science research, environment protection, meteorology, agriculture, mineral and petroleum exploration. Since hyperspectral image combines the character of image and spectrum, so it is applied in agricultural product quality, food safety and nondestructive detection. At home, Hong Tiansheng et al. built prediction model of Chinese pear quality based on hyperspectral imaging technique and artificial neural network [START_REF] Tiansheng | Non-destructive inspection of Chinese pear quality based on hyperspectral imaging technique[END_REF]. Zhao Jiewen et al. detected the subtle bruises on apple using hyperspectral image [START_REF] Zhao Jiewen | Detecting Subtle Bruises on Fruits with Hyperspectral Imaging[END_REF]. Liu Muhua et al. nondestructively detected soluble solids content and contamination on navel orange surface by hyperspectral laser-induced fluorescence imaging [START_REF] Enyou | Nondestructive Detection of Sugar Content on Navel Orange with Hyperspectral Imaging[END_REF][START_REF] Long | Detecting Pesticide Residue on Navel Orange Surface by Using Hyperspectral Imaging[END_REF]. At abroad, M. S. Kim et al. detected fecal contamination on apple, cantaloupes using hyperspectral fluorescence imagery [START_REF] Moon | Ns-scale time-resolved laser induced fluorescence imaging fordetection of fecal contamination on apples[END_REF][START_REF] Vargas | Safety Inspection of Cantaloupes and Strawberries Using Multispectral Fluorescence Imaging Techniques[END_REF][START_REF] Kim | Automated detection of fecal contamination of apples based on multispectral fluorescence image fusion[END_REF][START_REF] Lee | Hyperspectral imaging for detecting defect on apples[END_REF]. Juan Xing et al. detected surface defects on tomatoes by a hyperspectral imaging system [START_REF] Xing | Wavelength Selection for Surface Defects Detection on Tomatoes by Means of a Hyperspectral Imaging System[END_REF]. In experiment, there are usually tens to hundreds of acquired hyperspectral images, and the images need to be preprocessed. Although ENVI software offers interactive computing functions of spectral bands and spectrum, the same preprocessing of hundreds of images takes considerable time and is a very boring job. This article uses IDL to write a batch program module based on ENVI, which can achieve the normalization preprocessing of the spectral data.

IDL and ENVI redevelopment 2.1 IDL introduction

IDL is the ideal, timesaving solution for data analysis, data visualization, and software application development. The users can quickly and easily use this software to convert the data to images. The converted images can be simple color, full color or three-dimensional images. As ENVI software provides the function interface of IDL, so the users can use the built-in IDL functional components, IDL users function, or a custom program. However, these functions must be kept in a directory of ENVI path list to be automatically compiled.

Spectral data preprocessing

Because the uneven distribution of light source intensity under the different wave bands and the presence of dark current noise of camera, so the images may contain more noises in some wave bands, therefore, the hyperspectral images must be normalized. Under the same experimental condition, the image of a stand white plate was acquired. The dark image was acquired by completely covering the lens with its cap. The normalization preprocessing is achieved according to equation ( 1

ENVI redevelopment

The most common method of ENVI software redevelopment is using IDL to develop small application program modules, and call these modules. Meantime, these modules must be stored in the directory "X:\RSI\IDLXX\products\enviXX\save_add" to be compiled automatically. In this article, the hyperspectral files to be processed were saved in the directory "X:\original\", and a total of n hyperspectral files were named according to numerical order (such as DH1.raw, DH2.raw, ..., DHn.raw). In the folder,The standard white plate hyperspectral image (white.raw) and the dark hyperspectral image (black.raw) were saved in the same folder. After preprocessing, the hyperspectral files were stored in the directory "x: \ normalized \ folder", the program code is as follows:

; Building the patch processing program module of hyperspectral images "normalized". The function is to normalize the original images. ; Opening the file, option "no_realize " represents that the file will not be opened in available band list, and "fid" is the number of the file. envi_open_file, in_name0,/no_realize, r_fid=fid ; Opening the dark and standard white plate images.

in_name1=inpath+filename[0]

in_name2=inpath+filename [START_REF] Tiansheng | Non-destructive inspection of Chinese pear quality based on hyperspectral imaging technique[END_REF] envi_open_file, in_name1,/no_realize,r_fid=fid2 envi_open_file, in_name2,/no_realize,r_fid=fid3

; Accessing file's information, "ns" represents the sample number of the file, "nl" represents the number of lines, "nb" represent the number of bands, "xstart" and "ystart" represents the pixel coordinates of the top left corner in the image and the default value is (1,1). envi_file_query, fid, interleave=interleave,ns=ns, nl=nl, nb=nb,dims=dims, data_type=_datatype, xstart=_xstart, ystart=_ystart,SPEC_NAMES=SPEC_NAMES ; "dims" is the spectral range to be processed. "Ssn" represents the first file, and "esn" represents the last file. "sln" represents the first line of the file, and "eln" is the last line. tile_id=envi_init_tile(fid,pos,num_tiles=num_tiles, interleave=interleave>1,xs=dims [START_REF] Tiansheng | Non-destructive inspection of Chinese pear quality based on hyperspectral imaging technique[END_REF],xe=dims [START_REF] Zhao Jiewen | Detecting Subtle Bruises on Fruits with Hyperspectral Imaging[END_REF],ys=di ms [START_REF] Enyou | Nondestructive Detection of Sugar Content on Navel Orange with Hyperspectral Imaging[END_REF],ye=dims [START_REF] Long | Detecting Pesticide Residue on Navel Orange Surface by Using Hyperspectral Imaging[END_REF]) tile_id2=envi_init_tile(fid2,pos,num_tiles=num_tile s, interleave=interleave>1,xs=dims [START_REF] Tiansheng | Non-destructive inspection of Chinese pear quality based on hyperspectral imaging technique[END_REF],xe=dims [START_REF] Zhao Jiewen | Detecting Subtle Bruises on Fruits with Hyperspectral Imaging[END_REF],ys=di ms [START_REF] Enyou | Nondestructive Detection of Sugar Content on Navel Orange with Hyperspectral Imaging[END_REF],ye=dims [START_REF] Long | Detecting Pesticide Residue on Navel Orange Surface by Using Hyperspectral Imaging[END_REF]) tile_id3=envi_init_tile(fid3,pos,num_tiles=num_tile s, interleave=interleave>1,xs=dims [START_REF] Tiansheng | Non-destructive inspection of Chinese pear quality based on hyperspectral imaging technique[END_REF],xe=dims [START_REF] Zhao Jiewen | Detecting Subtle Bruises on Fruits with Hyperspectral Imaging[END_REF],ys=di ms [START_REF] Enyou | Nondestructive Detection of Sugar Content on Navel Orange with Hyperspectral Imaging[END_REF],ye=dims [START_REF] Long | Detecting Pesticide Residue on Navel Orange Surface by Using Hyperspectral Imaging[END_REF])

; Processing each block data, and saving the file in the directory "x:\normalized\" The image of pear at 680nm before preprocessing is shown in Figure 1. From figure 1, the obvious strip in the image is found easily. Figure 2 show the image of pear at 680nm after preprocessing, the quality of image is improved greatly.

Conclusion

By using IDL language and ENVI redevelopment, an extensible remote sensing system with perfect functions can be developed. Nowadays, this system is further expansion because more and more research achievements are integrated in it by scientific researchers. The strong abilities of IDL language and ENV redevelopment make the scientific researchers can release from boring programming work to invest more time and effort to actual research.

  pixel value of the image after normalized preprocessing; sample I --the pixel value of a sample image; reference I --the pixel value of standard white plate; dark I --the pixel value of the dark image。

  pro normalized ; Setting the path of hyperspectral files to be processed. inpath=' X:\original\' ; Setting the path of hyperspectral files having been processed. outpath=' X:\normalized\' ; Defining the array "filename" which was used to store the file name of dark image and standard white plate image. filename = ['black.raw', 'white.raw'] ; read the hyperspectral files, L represents the data is numerical type. for j=1L,nL do begin ; Converting data from numerical type to character type num=string(format='(i0)',j) ; Saving the path and file name of a specific hyperspectral file with "in_name" in_name0=inpath+num+'.raw' ; Printing the file name in IDL screen to express the processing course print,in_name0

  pos=lindgen(nb) dims=[-1l,ssn,esn,sln,eln] envi_file_query, fid2, interleave=interleave2 envi_file_query, fid3, interleave=interleave3 ; Setting the command blocks.

  openw, _unit, outpath+num+'.raw', /get_lun for i=0L, num_tiles-1 do begin data = envi_get_tile(tile_id, i) data2 = envi_get_tile(tile_id2, i) data3 = envi_get_tile(tile_id3, i) data4=(float(data)-float(data2))/( float(data3)float(data2)) writeu, _unit,data4 endfor free_lun, _unit ; Building the head file. envi_setup_head, fname=outpath+aa, ns=dims[2]-dims[1]+1,nl=dims[4]-dims[1]+1, nb=nb,data_type=4, offset=0, interleave=interleave,xstart=_xstart+dims[1], ystart=_ystart+dims[3],descrip=' Normalized file', /write ; Releasing the command block. envi_tile_done, tile_id envi_tile_done, tile_id2 envi_tile_done, tile_id3 envi_file_mng,id=fid,/remove envi_file_mng,id=fid2,/remove envi_file_mng,id=fid3,/remove ; Printing the number of a file which has been processed.

Figure. 1

 1 Figure.1 Image of pear at 680nm before preprocessing


Shijuan Fanf

Chao Yang

Zhiwei Wang

Automatic Control System for Highway Tunnel Lighting

Keywords: automatic control system, tunnel lighting, stepless control method, energy-saving

In order to solve the problems of large energy consumption existing in tunnel lighting control system, an automatic control system of tunnel lighting is designed based on stepless control method. The tunnel lighting control model is established based on "Specifications for Design of Ventilation and Lighting of Highway Tunnel (China)". Simulation of tunnel lighting control based on the established stepless control model is completed with Matlab. Compared with theoretical luminance data, simulation results show that the automatic control system can meet the luminance requirements of actual tunnel lighting, the luminance errors can be controlled less than 2%. Only considering maximum lighting values, the stepless controlled LED lamps can save more than 80% and 35% energy than HPS lamps and LED lamps respectively, and can save more than 20 % energy than four-steps controlled LED lamps.

Introduction

China H socialH economy and traffic undertaking are developing rapidly, tunnel traffic is becoming more and more necessary and important in mountainous areas of China, but the operating cost of tunnel traffic is huge, how to improve traffic safety performance and reduce operating cost of the tunnel traffic has become a focus issue that the China's transport department concerned. Tunnel lighting is an indispensable part to ensure driving safety and normal operation in tunnel traffic, and also is a key factor to reduce tunnel interior energy consumption [START_REF] Schreuder | Road Lighting for Safety[END_REF]. Therefore, the corresponding design specifications about tunnel lighting are issued in various countries, such as CIE (Commission International d'Eclairage), BS (Britain lighting standards) and IES (Illuminating Engineering Society of North America), etc [START_REF]CIE No88-2 Guide for the Lighting of Road Tunnels and Underpasses[END_REF]- [START_REF]BS 5489-2 Code of practice for the design of road lighting -Lighting of tunnels[END_REF]. To establish a safe, economical and energy-saving tunnel lighting system has important significance for sustainable development of China's highway engineering.

Design quality of tunnel lighting control system determines whether tunnel lighting design is excellent or not. The existing control methods include manual control, sequential control and automatic control methods. Manual and sequential control methods are easy to implement and more stable and reliable in practice, but the tunnel interior luminance can be not adjusted along with the changes of weather, traffic volume and vehicle speed, the both methods hardly have any energy-saving effect, as a result, much electric energy is wasted [START_REF] Huang | Energy saving tunnel lighting system based on PLC[END_REF]- [START_REF] Nagai | Energy-saving lighting system for road tunnel[END_REF].

Automatic control methods are widely used in modern tunnel lighting system, which can be divided into hierarchically control method and stepless control method according to adjustment continuity of tunnel light. At present, hierarchical control methods are often used in tunnel lighting control system in China before the application of LED (light-emitting diode) lamps in tunnel lighting, but some problems exist in the methods, the main problems are: 1) the automatic control levels can be done only from 2 to 3 because of the limited routing circuit, the parameters of environmental luminance, traffic volume and vehicle speed are only considered with maximum values at design stage, as a result, the lighting luminance of each section in tunnel is always in maximum state, lighting efficiency is evidently low, electric energy consumption is great; 2) the contradictions with driving H safetyH and tunnel monitor arise during the operation course [START_REF] Li | Energy-saving & Control of Tunnel Illumination[END_REF]- [START_REF] Zhi | Research on illumination energy saving technique of highway tunnel[END_REF]. In the paper, an automatic control system for tunnel lighting is designed based on the characteristics of LED lamps, especially the characteristics of control easiness compared with other lamps, which made the luminance in tunnel be adjusted dynamically along with the changes of environmental luminance, traffic volume and vehicle speed, and thus continuous tuning of tunnel lighting is achieved. The control system not only ensures operation safety of the tunnel, but also realizes energy-saving.

Tunnel lighting control strategy

The control strategy of tunnel lighting system is designed based on "Specifications for Design of Ventilation and Lighting of Highway Tunnel" [START_REF]JTJ 026.101999 Specifications for Design of Ventilation and Lighting of Highway Tunnel[END_REF] (hereinafter referred to as the "Design Specifications"), the principle is that the mathematical models of each section in tunnel are established based on the adaptation curve of the luminance in tunnel (as shown in Fig. 1), tunnel exterior environmental luminance, traffic flow and vehicle speed information. In accordance with the established mathematical models, the dynamic dimming control of LED lamps is conducted, and the luminance in tunnel is very close to the adaptation curve, energysaving is achieved with optimal control effect.

Stepless control is not absolute continuous dimming, but a more refined hierarchical control method. Environmental parameters with maximum values are generally considered in hierarchical control at tunnel lighting design stage, the effect of the changes of environmental parameters is neglected. Sometimes automatic control is achieved only from 2 to 3 levels because of the limited routing circuit.

Stepless control is closer to continuous dimming by adopting more refined levels [START_REF] Lv | The Application of Stepless Intelligent Control System to LED Illuminating Brightness in Tunnel[END_REF].

When planning tunnel lighting, five sections have to be considered: access zone, entrance zone, transition zone, interior zone and exit zone. There are different lighting requirements for different zones. In order to meet requirements of human eyes adaptation to luminance, logarithm dimming method for LED lamps is adopted: single-lamp 256-grade dimming, so that the whole tunnel lighting looks like a linear regulator dimming. Meanwhile, the threat of driving safety due to abrupt luminance change can be avoided by gradual control method. Triggering light dimming too often is not only conducive to the human eyes adaptability, and to some extent, service time of lamps is also reduced. Time-triggered model is resorted during the control process: information of environmental luminance, traffic volume and vehicle speed is collected and required luminance is calculated at interval of 3~5 minutes. In this paper, the mathematical models of the luminance of each section in tunnel are established on the data from "Design Specification" by a linear regression.

1) The mathematical model of the entrance zone: one-rank linear regression is adopted in MATLAB to make statistics analysis of the data from "Design Specifications" and fitted regression equations of luminance discount coefficient of the entrance zone are obtained. The luminance discount coefficient of the entrance zone under different traffic volumes and vehicle speeds is calculated by formula (1). 0.0004 0.0085 700 0.54 0.0002 ( 1.1) 12.91 700 2400 1700 0.0006 0.0107 2400

v Q v Qv k Q v Q - ≤ ⎧ ⎪ + - - ⎪ = < < ⎨ ⎪ - ≥ ⎪ ⎩ . ( 1
)
where v is vehicle speed, Q is traffic volume.

The luminance of entrance zone is calculated by the formula (2).

( )

th L k L S = ⋅ . ( 2
)
where L th is the entrance zone luminance (cd/m 2 ); L 20 (S) is the tunnel exterior environmental luminance (cd/m 2 ).

The entrance zone luminance under different traffic volumes and vehicle speeds is calculated by formula [START_REF]ANSI/IESNA RP-22-96 American National Standard Practice for Tunnel Lighting[END_REF].

th v L S Q v Qv L L S Q v L S Q - × ≤ ⎧ ⎪ + - - ⎪ = × << ⎨ ⎪ - × ≥ ⎪ ⎩ . ( 3
)
2) The mathematical model of the interior zone: the interior zone luminance is relevant to traffic volume and vehicle speed, the tunnel exterior environmental luminance has no effect on it. In order to reduce the luminance calculation error, the second-order linear regression is adopted to fit the interior zone luminance values. The interior zone luminance under different traffic volumes and vehicle speeds is calculated by formula (4).

in v v Q v vQ v v Q Q L Q v v Q ⎧ - + ≤ ⎪ + - - + + ⎪ = < < ⎨ ⎪ ⎪ - + ≥ ⎩ . ( 4
)
3) The transition zone luminance depends on the entrance luminance. The transition zone is composed of three sections: L tr1 , L tr2 , L tr3 , and the corresponding luminance of each section is calculated by L tr1 =0.3L th , L tr2 =0.1L th , L tr3 =0.035L th , respectively, according to the "Design Specifications".

4) The exit luminance is five times of the interior zone luminance, L out =5L in .

2

Control system structure

In order to meet the demands of tunnel lighting and energy-saving better, stepless control method is adopted in the tunnel lighting control system. The control system is composed of vehicle detectors, luminance detectors, data converters, lighting control computer, dimming controllers and LED lamps. The structure block diagram is shown in Fig. 2.

Environmental luminance, traffic volume and vehicle speed information are collected by vehicle detectors and luminance detectors. The data converted by data converters from collected information are sent to lighting control computer installed in tunnel control room. According to the predetermined dimming logic in lighting control computer, the luminance of each section in tunnel is calculated, the required dimming values for dimming controllers of each node are calculated in the control computer in accordance with the preceding luminance calculated and power curve of LED lamps. The control computer sent control commands to dimming controllers, LED lamps power are adjusted by the commands received, and fed relevant information back to lighting control computer, and continuous light tuning of tunnel lighting is achieved. VCCS (voltage control current source) is adopted in the dimming controllers of tunnel LED lamps. VCCS is output-current controllable power which uses direct current signal voltage to control current output average, the schematic diagram of VCCS is shown in Fig. 3. VCCS directly controls duty factor of primary switch tube of power supply after current-signal's being sampled, isolated and amplified, isolation transformer converts electric energy of different duty factors into direct pulse current, the direct pulse current is filtered by low pass filter and converted into direct current that changes with control signal. In this way, the output current is controlled.

Main control program flow chart

The flow chart of the main tunnel lighting control program is shown in Fig. 4, the steps are as follows:

(1) Initializing sub-modules of the system, reading luminance information and vehicle information, and judging whether the manual control is enabled (the automatic control is implemented by default).

(2) If the automatic control is implemented, judging whether the tunnel is in normal state, if in abnormal state, calling the special control procedures, alarming and ending the program; if in normal state, calling control procedures of automatic dimming directly and outputting control instructions, so one dimming course is completed.

(3) Judging whether the program is stopped by instruction of external trigger, if not, collecting the new data at interval of 3~5 minutes, if yes, ending the program. The mathematical model and control flow are converted into the corresponding control program and embedded in the lighting control computer to realize automatic dimming control of tunnel lighting.

Simulation

According to the mathematical model and control method, combining with the environmental data, a simulation experiment and analysis is done. Setting parameter values of environmental luminance, traffic volume and vehicle speed in different time range during sunny day, 20 sets of simulation luminance data of each section in tunnel are collected, part data are shown in TableⅠ(only 5 sets of sample data are listed in the paper). The theoretical luminance data of each section in tunnel are calculated according to the established mathematical model and environmental parameters set, 5 sets of data are shown in Table II. Comparing the data of Table I and Table II, the simulation luminance data are very close to the theoretical luminance data, the error range can be controlled at 2%, even less. The maximum value of HPS (high pressure sodium) power curve is normalized(setting to 1), other power curve values are obtained compared with HPS power value. The power curves of tunnel lighting in sunny days are shown in Fig. 5. The areas surrounded by the curves and horizontal axis show relative power consumption of different control methods. It can be seen from the graph that 40%~50% energy-saving is achieved by using LED lamps to replace HPS lamps. Energy-saving effect of stepless control to LED lamps is very obvious, compared with HPS lamps and LED lamps only considering maximum lighting value, the energy-saving is more than 80% and 35% respectively. Even compared with 4-steps controlled LED lamps, stepless controlled LED lamps can save more than 20 % energy.

Conclusion

Tunnel lighting is an important component of highway traffic; its primary function is to ensure driving safety and comfort during both daytime and nighttime in tunnel. However, energy-saving is also great of importance and cannot be neglected. In this paper, an automatic control system of tunnel lighting is designed based on superior performance of LED lamps and stepless control. The designed control system can meet the lighting requirements very well and overcome the problem of large electricity waste caused by traditional lighting control methods. The impact of the changes of environmental luminance, traffic flow and vehicle speed is considered, continuous dimming of tunnel lighting is achieved, and energy-saving effect is remarkable.

Fig. 1 .

 1 Fig.1. Theory demand curve of tunnel lighting.

Fig. 2 .

 2 Fig. 2. Block diagram of tunnel lighting auto-control.

Fig. 3 .

 3 PhotoelectricIsolation

Fig. 4 .

 4 Fig. 4. Flow chart of tunnel lighting control system.

Fig. 5 .

 5 Fig. 5. Power curves of tunnel lighting in sunny days.

Table I .

 I Simulation luminance data (partly).

	Exterior	Traffic	Vehicle
	luminance (cd/m 2 )	(vehicles/h) volume	(km/h) speed	L th	L tr1	L tr2	L tr3	L in	L out
	4000	500	65	73	21.9	7.3	2.6	1.8	9.0
	4000	1000	75	99	29.7	9.9	3.5	2.4	12.0
	3000	1500	85	96	28.8	9.6	3.4	3.6	18.0
	3000	2500	95	142	42.6	14.2	5.0	7.9	39.5
	2000	1500	85	67	20.1	6.7	2.3	4.1	20.5
	2000	1000	75	51	15.3	5.1	1.8	2.3	11.5

Table II .

 II Simulation luminance data (partly).

	Exterior	Traffic	Vehicle
	luminance (cd/m 2 )	(vehicles/h) volume	(km/h) speed	L th	L tr1	L tr2	L tr3	L in	L out
	4000	500	65	70	21	7	2.5	1.7	8.5
	4000	1000	75	95	28.5	9.5	3.3	2.2	11
	3000	1500	85	97	29.1	9.7	3.4	3.8	19
	3000	2500	95	139	41.7	13.9	4.9	7.8	39
	2000	1500	85	65	19.5	6.5	2.3	3.8	19
	2000	1000	75	48	14.4	4.8	1.7	2.2	11

School of Mechatronic Engineering, East China Jiaotong University Nanchang, P.R. China

Acknowledgements. This work is supported by Key Laboratory of Ministry of Education for Conveyance and Equipment (East China Jiaotong University).