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COMPUTER AND COMPUTING TECHNOLOGIES IN AGRICULTURE, VOLUME I
IFIP – The International Federation for Information Processing IFIP was founded in 1960 under the auspices of UNESCO, following the First World Computer Congress held in Paris the previous year. An umbrella organization for societies working in information processing, IFIP's aim is two-fold: to support information processing within its member countries and to encourage technology transfer to developing nations. As its mission statement clearly states,
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COMPUTER AND COMPUTING TECHNOLOGIES IN AGRICULTURE, VOLUME I First IFIP TC 12 International Conference on Computer and Computing Technologies in Agriculture (CCTA 2007), Wuyishan, China, August 18-20, 2007 Edited by DAOLIANG LI China Agricultural University
Edited by Daoliang Li
ISSN: 1571-5736 / 1861-2288 (Internet)
ISBN: 978-0-387-77250-9 eISBN: 978-0-387-77251-6 Printed on acid-free paper
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Computer and Computing Technologies in Agriculture, Vol. 1
in Computer Science)
Library of Congress Control Number: 2007940858
p. cm. (IFIP International Federation for Information Processing, a Springer Series
Foreword ............................................................................................................ xiii Organizing Committee.........................................................................................xv Program Committee .......................................................................................... xvii Secretariat........................................................................................................... xix
Using Neural Networks Under Uniaxial Cold Pressing ........................................1 Xiao Zheng, Guoxiang Lin, Dongping He, Jingzhou Wang
Parameterized Computer Aided Design of Stubble Cleaner ...............................11
Regional Country Information Service Platform Based on Hybrid Network................................................................................................................19
Songbin Zhou, Guixiong Liu, Taobo Cheng
Shumin Zhou, Guoyun Zhong, Tiantai Zhang
from Japan, South Korea and India .....................................................................35
Design and Implementation of a Mobile Management System for Campus Server ...................................................................................................................43
Shijue Zheng, Zhenhua Zheng
Muhua Liu, Luring Zhang, Enyou Guo
A Fast Longest Common Subsequence Algorithm for Biosequences Alignment.............................................................................................................61
Wei Liu, Lin Chen
CONTENTS
Measurement and Prediction of Stress-strain for Extruded Oilseed
Lige Wen,
The Short Message Management System Based on J2ME .................................27
The Enlightenment to China of E-government Application in Rural Areas
Jianqiao Li, Xiuzhi Zhang, Benard Chirende
Wenyun Liu, Jinglei Wang
Hyperspectral Laser-induced Fluorescence Imaging for Nondestructive Assessing Soluble Solids Content of Orange ......................................................51
Chinese Agricultural Status, Issues and Strategies of the Development of Electronic Commerce ......................................................................................71
Hua Jiang, Jing Yang
Research on Monitoring Technology of Digital Reservoir .................................85 Chengming Zhang, Jixian Zhang, Yong Liang, YongXiang Sun, Zhixin Xie
Hongxia Cui, Zongjian Lin, Jinsong Zhang
Xuri Yin
Xuesong Suo, Nan Shi
121
Analysis of Virtual Reality Technology Applications in Agriculture................133
Hailin Li Research and Development of the Information Management System
Hao Zhang, Lei Xi, Xinming Ma, Zhongmin Lu, Yali Ji, Yanna Ren
The Design of Fruit Automated Sorting System ...............................................165
Pingju Ge, Qiulan Wu, Yongxiang Sun Constructing an Informational Platform for Family and School Using GPRS .......................................................................................................171
Yongping Gao, Yueshun He Reduction in Agricultural Tax and the Income Growth of Rural Residents: An Empirical Study............................................................................................179
Ruiping Xie, Fanling Sun Explore a New Way to Convert a Recursion Algorithm into
Yongping Gao, Fenfen Guan
Research on Low Altitude Image Acquisition System........................................95
A Threshold-based Similarity Relation Under Incomplete Information ...........103
Web-based Expert System of Wheat and Corn Growth Management ..............111
Bing Zhang, ShouQi Yuan, JianSheng Zhang, Hong Li
of Agricultural Science and Technology to Farmer Based on GIS ...................141
Multicast in Mobile ad hoc Networks................................................................151
a Non-recursion Algorithm ................................................................................187
vi Contents
Study of Corn Optimization Irrigation Model by Genetic Algorithms .............
Zhijun Wang, Yong Liang, Lu Wang
The Construction and Implementation of Digitization Platform for Precision Feeding of Commercial Pig ...............................................................................195
Shihong Liu, Huoguo Zheng, Haiyan Hu, Yunpeng Cui, Xi Su Study on the Application of Digital Irrigation Area System .............................205
Yong Liang, Jiping Liu, Yanling Li, Chengming Zhang, Mingwen Ma
Fengchang Xue, Zhengfu Bian
Region ................................................................................................................223 Wei Wu, Xuan Wang, Deti Xie, Hongbin Liu
An Efficient New Method on Accurately Estimating Galileo VPL ..................231
Ying Guo, Xiushan Lu
Irsan Suryadi, Rohani Jahja Widodo
Zhengmiao Xie, Jing Li, Weihong Wu
Research on Dynamic Change of Grassland in West Jilin Province Based
Study on Linear Appraisal of Dairy Cow’s Conformation Based on Image
Dongping Qian, Wendi Wang, Xiaojing Huo, Juan Tang Research of RFID Middleware in Precision Feeding System of Breeder Swine................................................................................................
Weiwei Sun, Xuhong Tian , Minjie Jiang
Contents vii
Soil Water Content Forecasting by Support Vector Machine in Purple Hilly
Control Systems in Our Daily Life ....................................................................239
Application of GIS and Geostatistics to Characterize Spatial Variationof Soil Fluoride on Hang-Jia-Hu Plain, China...................................................253
Benhai Xiong, Qingyao Luo, Jianqiang Lu, Liang Yang
Hua Gao, Yaqin Wang, Pingju Ge
Hua Gao, Yaqin Wang, Zhijun Wang
Processing ..........................................................................................................303
Rice Shape Parameter Detection Based on Image Processing ..........................287
Representation and Calculation method of Plant Root......................................295
313
Nanyan Ling Zhiming Liu,
Development for Breeding Performance Management System on Pig Farms .....267
on 3S Technology..............................................................................................277
GIS Combined with MCE to Evaluate Land Quality ........................................215
Simulating Land Use/Cover Changes of Nenjiang County Based
Baoying Ye, Zhongke Bai
Differentially Gene Expression in the Brain of Common Carp
Liqun Liang, Shaowu Li, Yumei Chang, Yong Li, Xiaowen Sun, Qingquan Lei
Shuo Xu, Xin An, Lan Tao
Nengfu Xie, Wensheng Wang, Yong Yang Research on Precision Irrigation in Western Semiarid Area of Heilongjiang
Q. X. Jiang, Q. Fu, Z. L. Wang Decision Support System for Risk Management in Aquatic Products
Feng Wang, Xiaoshuan Zhang, Cheng Tan, Chuanli Zhuang, Zetian Fu Design of Data Center’s High Reliability in Large Agricultural
Hanxing Liu, Yingjie Kuang, Caixing Liu, Lei Xiao, Guomao Xu
Mingtian Wang, Xianfeng He, Chunlu Li, Yongkang Luo, Jinlian Zhang, Liping Ou, Qin Xue , Yingwei Ai
Xianfeng He, Mingtian Wang, Yuan Yang, Yongkang Luo, Yingwei Ai
Computer Simulation of the Pesticide Deposition Distribution
Wanzhang Wang, Tiansheng Hong, Wenyi Liu, Xiangfu Li
viii Contents
on CA-Markov Model........................................................................................321
An Improved Fast Brain Learning Algorithm ...................................................341
Ontology-based Agricultural Knowledge Acquisition and Application ...........349
Province in China Based on GIS .......................................................................359
Enterprise ...........................................................................................................379
Export Trade, China...........................................................................................371
Role’s Functions in CMS...................................................................................389
Research on Quality Standard of Rural Information .........................................397
Implementation Scheme of Agricultural Content Management System...........407
in Horizontal Direction Spray............................................................................425
Xianfeng He, Chunlu Li, Mingtian Wang, Yongkang Luo, Yingwei Ai
Xianfeng He, Mingtian Wang, Jinlian Zhang, Yongkang Luo
Content Share in CMS .......................................................................................419
(Cyprinus carpio) Response to Cold Acclimation ............................................331
Ping Yang, Shu Dai, Xiuhua Wu, Yong Yang
Zhongxin Chen, Einar Holm, Huajun Tang, Kalle Mäkilä, Wenjuan Li, Shenghe Liu
Junjing Yuan, Daoliang Li, Hongwen Li
Weili Wang, Xiaodong Zhang, Xiao He
Zhihua Diao, Chunjiang Zhao, Xiaojun Qiao, Cheng Wang, Gang Wu, Xin Zhang
Qiulan Wu, Yong Liang, Xia Geng, Wenjie Li, Yanling Li
Yongjian Yang, Xu Yang, Chijun Zhang
Yan Zhang, Yong Liang, Chengming Zhang, Qiulan Wu, Pingjiu Ge Research of Automatic Monitoring System of Reservoir Based
Chengming Zhang, Jixian Zhang, Yong Liang, Yan Zhang, Guitang Yin
Tinghong Zhao, Zibin Man, Xueyi Qi The Research on Grain Reserve Intelligent Audit Method
Ying Lin, Xiaohui Jiang
Ying Lin, Liang Ge
Contents ix
in Rural Power Terminal System of Power Quantity Collection ......................433
How to Develop Rural Informatization in China ..............................................449
Study on Suitability Evaluation Model of New Maize Varieties ......................457
An Agent-based Population Model for China ...................................................441
Human-computer Interface Development of Wireless Monitoring System Based on MiniGUI ................................................................................471
Research of Sluice Monitoring System Based on GPRS and PLC ...................479
Self-adaptive Fuzzy Decision Map Matching Algorithm Based
Construction of Agricultural Products Logistics Information System
on GIS Buffer in LCS ........................................................................................487
on Embedded System.........................................................................................503
and GSM ............................................................................................................515
and Implementation in Three-dimensional Stores .............................................525
Study on Real-time Video Transportation for National Grain Depot ...............533
The Hardware Research of Dual-port RAM for Main-spare CPU
Based on .Net and Wap......................................................................................495
Long-range Monitoring System of Irrigated Area Based on Multi-Agent
Ying Lin, Yang Fu Polymorphism of Microsatellite Sequence within ABC Transporter
Lin Liu, Chengyun Li, Jing Yang, Jinbin Li, Yuan Su, Yunyue Wang, Yong Xie, Youyong Zhu
Yong Xie, Youyong Zhu
Yong Yang, Shuai Zhang
Huanliang Xu, Haiyan Jiang, Shougang Ren, Xiaojun Liu, Weixin Cao Geospatial Computational Grid for Digital Forestry
Guang Deng, Xu Zhang, Quoqing Li, Zhenchun Huang
Changshou Luo, Sufen Sun, Junfeng Zhang, Qiang Zuo, Baoguo Li
Ranbing Yang, Suhua Liu, Jie Liang, Shuqi Shang New Fast Detection Method of Forest Fire Monitoring and Application
Jianzhong Feng, Huajun Tang, Linyan Bai, Qingbo Zhou, Zhongxin Chen A Scheme for Share and Exploitation of Network Agricultural Information Based on B/S Structure ......................................................................................
Huitao Liu, Limei Tan, Yuan Yao, Qing Wang, Hongsheng Zhang,
Zhongyi Wang, Lan Huang, Xiaofei Yan, Cheng Wang, Zhilong Xu, Ruifeng Hou, Xiaojun Qiao
x Contents
Chengyun Li, Lin Liu, Jing Yang, Jinbin Li, Zhang Yue, Yunyue Wang, 559
Research and Design on Domain-agriculture- crops Software Architecture Oriented Adaptive Model ..................................................................................573
with the Interoperability.....................................................................................585
Using an Improved Genetic Algorithm .............................................................593 Simulating Soil Water and Solute Transport in a Soil-wheat System
Information Technology Speeding up Circulation of Rural Economy..............605
Guanglu Zhang, Jintong Liu
Based on FY-1D/MVISR Data ..........................................................................613
A Theory Model for Description of the Electrical Signals in Plant Part I ........637
Polymorphism of Microsatellite Sequence within Protein Kinase ORFs
The Key of Bulk Warehouse Grain Quantity Recognition: Rectangular Benchmark Image Recognition.....................................................543
Approach of Developing Spatial Distribution Maps of Soil Nutrients .............565
629
Genes in Phytopathogenic Fungus, Magnaporthe grisea ..................................553
in Phytopathogenic Fungus, Magnaporthe grisea .............................................
Development of a Data Mining Application for Agriculture Based
Jiejun Huang, Yanbin Yuan, Wei Cui, Yunjun Zhan
Ping Liang, Hongping Zhou, Jiaqiang Zheng Research on Machine Vision Based Agricultural Automatic
Bin Liu, Gang Liu, Xue Wu
A Bioeconomic Model by Quantitative Biology to Estimate
Hui Yuan, Surong Xiao, Qiujuan Wang, Keliang Wu
Chang Yi, Yaozhong Pan, Jinshui Zhang
Zhongbin Su, Ping Zheng, Hongmin Sun, Jicheng Zhang, Xiaoming Li Study on Maize Leaf Morphological Modeling and Mesh Simplification
Xinyu Guo, Chunjiang Zhao, Boxiang Xiao, Shenglian Lu, Changfeng Li Research and System Realization of Food Security Assessment in Liaoning
Changli Zhang, Shuqiang Liu, Junlong Fang, Kezhu Tan
Jingbo Zhao, Long Chen, Haobin Jiang, Limin Niu
Contents xi
on Bayesian Networks .......................................................................................645
Forecast Research of Droplet Size Based on Grey Theory ...............................653
Guidance Systems ..............................................................................................659
Swine Production ...............................................................................................667
An Integrated Approach to Agricultural Crop Classification Using SPOT5 HRV Images.......................................................................................................677
Study on Establishment Soybean Controllable Structural Model .....................685
of Surface ...........................................................................................................695
Crop Disease Leaf Image Segmentation Method Based on Color Features .....713
Bin Xi, Yunhao Chen, Hongchun Cai, Yang Liu
Lidi Wang, Tao Yang, Youwen Tian
Research on the Spatial Variability of Soil Moisture Based on GIS.................719
Design and Full-car Tests of Electric Power Steering System..........................729
Province Based on Grey Model .........................................................................703
The papers in this volume comprise the refereed proceedings of the the First International Conference on Computer and Computing Technologies in Agri-
This conference is organized by China Agricultural University, Chinese Society of Agricultural Engineering and the Beijing Society for Information Technology in Agriculture. The purpose of this conference is to facilitate the communication and cooperation between institutions and researchers on theories, methods and implementation of computer science and information technology. By researching information technology development and the re-sources integration in rural areas in China, an innovative and effective approach is expected to be explored to promote the technology application to the development of modern agriculture and contribute to the construction of new countryside.
The rapid development of information technology has induced substantial changes and impact on the development of China’s rural areas. Western thoughts have exerted great impact on studies of Chinese information technology develop-ment and it helps more Chinese and western scholars to expand their studies in this academic and application area. Thus, this conference, with works by many prominent scholars, has covered computer science and technology and information development in China’s rural areas; and probed into all the important issues and the newest research topics, such as Agricultural Decision Support System and Expert System, GIS, GPS, RS and Precision Farming, CT applications in Rural Area, Agricultural System Simulation, Evolutionary Computing, etc. In the fol-lowing sessions, this conference could hopefully set up several meeting rooms to provide an opportunity and arena for discussing these issues and exchanging ideas more effectively. We are also expecting to communicate and have dia-logues on certain hot topics with some foreign scholars.
With the support of participants and hard working of preparatory committee, the conference achieved great success on the participation. We received around 427 submitted papers and 180 accepted papers will be published in the Springer Press. It has evidenced our remarkable achievements made in our studies of the New Period, forming a necessary step in the development of the research theory in China and a worthy legacy for information technology studies in the new century. The conference is planned to be organized annually. We believe that it can provide a platform for exchanging ideas and sharing outcomes and also contribute to China’s agricultural development.
Finally, I would like to extend the most earnest gratitude to our co-sponsors, Chinese Society of Agricultural Engineering and the Beijing Society for Infor-mation Technology in Agriculture, also to Nongdaxingtong Technology Ltd., all members and colleagues of our preparatory committee, for their generous efforts,
FOREWORD
culture (CCTA 2007), in Wuyishan, China, 2007.
hard work and precious time! On behalf of all conference committee members and participants, I also would like to express our genuine appreciation to Fujian Provincial Agriculture Department, Nanping City Bureau of Agriculture and Wuyishan City government. Without their support, we can not meet in such a beautiful city.
This is the first in a new series of conferences dedicated to real-world
world. The wide range and importance of these applications are clearly indicated by the papers in this volume. Both are likely to increase still further as time goes
Chair of programme committee, organizing committee
xiv Foreword
Daoliang Li
by and we intend to reflect these developments in our future conferences.
applications of computer and computing technologies in agriculture around the
Chair
Prof. Daoliang Li, China Agricultural University, China
Members [in alpha order] Mr. Chunjiang Zhao, Director, Beijing Agricultural Informatization Academy,
China Mr. Ju Ming, Vice Section Chief, Foundation Section, Science & Technology
Department, Ministry of Education of the People’s Republic of China Prof. Haijian Ye, China Agricultural University, China Prof. Jinguang Qin, Chinese Society of Agricultural Engineering, China Prof. Qingshui Liu, China Agricultural University, China Prof. Rengang Yang, China Agricultural University, China Prof. Renjie Dong, China Agricultural University, China
Prof. Wanlin Gao, China Agricultural University, China Prof. Weizhe Feng, China Agricultural University, China
in Agriculture Director of EU-China Center for Information & Communication technologies
Prof. Songhuai Du, China Agricultural University, China
ORGANIZING COMMITTEE
Chair
Prof. Daoliang Li, China Agricultural University, China
Members [in alpha order]
Dr. Alex Abramovich, Maverick Defense Technologies Ltd., Israel Dr. Boonyong Lohwongwatana, Asian Society for Environmental Protection
(ASEP), Thailand Dr. Feng Liu, Mercer University, GA, USA Dr. Haresh A. Suthar, Industry (Masibus Automation & Instrumentation (p)) Ltd.,
India Dr. Javad Khazaei, University of Tehran, Iranian
Dr. Joanna Kulczycka, Polish Academy of Sciences Mineral and Energy Eco-nomy Research Institute, Poland
Dr. John Martin, University of Plymouth, Plymouth, UK
Dr. Pralay Pal, Engineering Automation Deputy General Manager (DGM), India Dr. Shi Zhou, Zhejiang University, China
Dr. Soo Kar Leow, Monash University, Malaysia Dr. Wenjiang Huang, National Engineering Research Center for Information
Technology in Agriculture, China Dr. Yong Yue, University of Bedfordshire, UK Dr. Yuanzhu Zhang, Suzhou Center of Aquatic Animals Diseases Control, China Mr. Weiping Song, DABEINONG Group
Athens, Greece Prof. Andrew Hursthouse, University of Paisley, UK
Director of EU-China Center for Information & Communication technologiesin Agriculture
Dr. Jinsheng Ni, Beijing Oriental TITAN Technology. Co. Ltd.
Dr. Sijal Aziz, Executive Director WELDO, Pakistan
Dr. Kostas Komnitsas, Technical University of Crete, Greece
Prof. A.B. Sideridis, Informatics Laboratory of the Agricultural University of
PROGRAM COMMITTEE
Dr. M. Anjaneya Prasad, College of Engg. Osmania University, India
Program Committee
Prof. Apostolos Sarris, Institute for Mediterranean Studies, Greece Prof. Chunjiang Zhao, China National Engineering Center for Information
Technology in Agriculture, China Prof. Dehai Zhu, China Agricultural University, China Prof. Fangquan Mei, Institute of Information, China Agricultural Science, China Prof. Gang Liu, China Agricultural University, China Prof. Guohui Gan, Institute of Geographic Sciences and Natural Resources Prof. Guomin Zhou, Institute of Information, China Agricultural Science, China Prof. Iain Muse, Development into Community Cooperation Policies and
International Research Areas, Belgium Prof. Jacques Ajenstat, University of Quebec at Montreal, Canada Prof. K.C. Ting, Department of Agricultural and Biological Engineering,
University of Illinois at Urbana-Champaign Prof. Kostas Fytas, Laval University, Canada Prof. Liangyu Chen, Countryside Center, Ministry of Science & Technology,
China Prof. Linnan Yang, Yunnan Agricultural University, China Prof. Liyuan He, Huazhong Agricultural University (HZAU), China Prof. Maohua Wang, member of Chinese Academy of Engineering, China
Agricultural University, China Prof. Maria-Ioanna Salaha, Wine Institute-National Agricultural Research
Foundation, Greece Prof. Michael Petrakis, National Observatory of Athens, Greece Prof. Michele Genovese, Unit Specific International Cooperation Activities,
International Cooperation Directorate, DG Research, UK Prof. Minzan Li, China Agricultural University, China Prof. Nigel Hall, Harper Adams University College, England Prof. Raphael Linker, Civil and Environmental Engineering Dept., Technion Prof. Rohani J. Widodo, Maranatha Christian University, Indonesia Prof. Xiwen Luo, South China Agricultural University, China Prof. Yanqing Duan, University of Bedfordshire, UK Prof. Yeping Zhu, Institute of Information, China Agricultural Science, China Prof. Yiming Wang, China Agricultural University, China Prof. Yu Fang, Information Center, Ministry of Agriculture, China Prof. Yuguo Kang, Chinese cotton association, China Prof. Zetian Fu, China Agricultural University, China Prof. Zuoyu Guo, Information Center, Ministry of Agriculture, China
xviii
Secretary-general
Liwei Zhang (China Agricultural University, China)
Secretaries
Xiuna Zhu (China Agricultural University, China) Yanjun Zhang (China Agricultural University, China) Xiang Zhu (China Agricultural University, China) Liying Xu (China Agricultural University, China) Bin Xing (China Agricultural University, China) Xin Qiang (China Agricultural University, China) Yingyi Chen (China Agricultural University, China) Chengxian Yu (China Agricultural University, China) Jie Yang (China Agricultural University, China) Jing Du (China Agricultural University, China)
Baoji Wang (China Agricultural University, China)
SECRETARIAT
MEASUREMENT AND PREDICTION OF
STRESS-STRAIN FOR EXTRUDED OILSEED
USING NEURAL NETWORKS UNDER
Xiao Zheng,*1 , Guoxiang Lin
1 , Dongping He2 , Jingzhou Wang
1
1 Department of Mechanical Engineering, Wuhan Polytechnic University, Wuhan, Hubei
2 Department of Food Science and Engineering, Wuhan Polytechnic University, Wuhan,
* Corresponding author, Address: Department of Mechanical Engineering, Wuhan
83956425, Fax: +86-27-83956425, Email: [email protected]
Abstract: A visualization of testing apparatus was developed to measure property of
oilseeds relevant to physical mechanics during mechanical pressing for oil
extraction. Stress-strain relationships were measured for extruded peanut,
soybean, sesame and linseed compressed at thirteen pressures under uniaxial
cold pressing. The prediction model of the stress-strain relationship was
developed based on BP neural network. Results indicated that the stress-strain
relationships were nonlinear. Over 50% strains for extruded soybean, sesame
and linseed occurred at stress below 20MPa. Over 60% strain for extruded
peanut occurred at stress below 10MPa. No more than 13% strain occurred at
stress over 20MPa for extruded soybean sesame and linseed, and no more than
13% strain occurred at stress over 10MPa for extruded peanut. The maximum
error between prediction and measurement for the stress-strain relationship was
less than 0.0084 and the maximum training times was less than 88.
Keywords: measurement, prediction, stress-strain, neural networks, oilseed, cold pressing
1. INTRODUCTION
Peanut, soybean and sesame oil are important edible oil in the world. The
mechanical pressing is the most common method for oil extraction in the world.
Polytechnic University, Wuhan 430023, Hubei Province, P. R. China, Tel: +86-27-
Zheng, X., Lin, G., He, D. and Wang, J., 2008, in IFIP International Federation for Information Processing, Volume 258; Computer and Computing Technologies in Agriculture, Vol. 1; Daoliang Li; (Boston: Springer), pp. 1–10.
Province, P. R. China, 430023
Hubei Province, P. R. China, 430023
UNIAXIAL COLD PRESSING
2 Vegetable oilseed expresses complex mechanics behavior during pressing
oilseeds must be thermal (cooking) pretreatments before pressing, which is
called the hot pressing (Rasehom et al., 2000; Bargale et al., 1999, 2000). More
recently, the cold pressing for oil extraction, which needn’t be cooked prior to
pressing, is very popular in China as well as in other many countries. The main
reason for popularity of the cold pressing is that the cold pressings yields limpid
cold pressing is inefficient with lower throughputs and higher residual oil
contents in the defiled cake. It indicates that the oil press used to the hot pressing
needs further improve for the purpose of the cold pressing (Rasehom et al.,
2000; Zheng Xiao et al., 2004).
The stress-strain relationship is the most important performance of physical mechanics for extruded oilseeds. The stress-strain model for
extruded oilseeds by cold pressing is essential to rigorous theoretical
single rapeseeds. Sukumaran et al. (1989) have studied bulk properties of rapeseeds under compression. However, the research relating to stress-strain
relationship for extruding oilseed has not yet been reported in the world up
relationship for oilseeds due to the complexity of physical mechanics performance during pressing (Zheng Xiao et al., 2004). At present,
multivariable nonlinear regression analysis is most common method to develop empirical formula to predict stress-strain relationship for complex material. However, the difference in the variable used in the analytical model
and the details of the experiment will lead to significant diversity in the calculation formulas, and furthermore there is usual a difficulty to determine suitable regression equation used in multiple regression analysis, which
requires considerable technique and experience due to understanding of the data characteristic of stress-strain experiment. The objectives of this study were to measure stress-strain for extruding oilseeds by uniaxial cold
pressing, and develop neural network modeling to predict the stress-strain relationship.
Xiao Zheng et al.
et al., 2000; Zheng Xiao et al., 2004). However, compared to the hot pressing, the
color and fruity oilseed oil with lower phosphorus and fatty acid (Rasehom
et al., 2004). Davison et al. (1975, 1979) have studied mechanical properties of
problems of permeability, differential equation for seepage (Zheng Xiao
et al., 2004). till now except research for rapeseed and dehulled rapeseed (Zheng Xiao
It is found very difficult to develop the theoretical model for stress-strain
analysis of mechanisms and physical processes. It lays a foundation for
(Mrema et al., 1985). The conventional method of oil extraction suggests that
3
2. STRESS-STRAIN EXPERIMENT
2.1 Design of visualized compression cell
A visualization of test apparatus used for the experiment was specially designed. Its schematic diagram is shown in Fig. 1. It mainly consists of a
plate, a porous stone and a base plate. The test apparatus is mounted in a universal hydraulic test machine capable of applying compressive loads of
95mm deep bore through which the loading piston compresses sample. The visual cylinder is made of plexiglas. An outer cylinder made of mild steel is
of oilseed samples can be observed through the visual inner cylinder. Support plate made of stainless-steel with several 3 mm diameter traverse holes distributed uniformly is designed to prevent porous stone from breaking. In order to ensure uniform fluid pressure within oilseed cakes, both the bottom of loading piston and the top of base plate are provided with
oilseed sample are respectively provided with a porous stone in order to expel liquid (including oil and water) and air from oilseed during compression.
2.2 Measurement of stress-strain
A 30g sample was chosen as testing specimen for the experiment of peanut, soybean, sesame and linseed. On the top and the bottom of oilseed
Measurement and Prediction of Stress-strain for Extruded Oilseed
loading piston, an outer cylinder, an inner cylinder, a sealing ring, a support
outer cylinder is provided with two observed windows with a 20mm width × 25mm height. The performance and phenomenon of compressive process
300KN. The pressing chamber is provided with a 44mm diameter ×
radial and circular grooves 5 mm width × 5 mm depth. The top and bottom of
essential to visual cylinder in order to increase its strength and rigidity. The
Fig. 1. Schematic diagram of visualized compression cell
4 specimen two fast speed filter papers were respectively inserted for the purpose of preventing porous stones from blocking up with bits of broken oilseed. After the specimen was poured into the compression cell, the cell
was mounted in a computer-controlled precision universal test machine. Initial thickness of specimen of peanut, soybean, sesame and linseed were measured, which were 33.3mm, 28.2mm, 29.8mm and 27.7mm respectively.
Equal rate of applied pressure (0.1MPa.s-1) was used in the experiment.
and under double surface for flow of fluids through a porous stone. Each
desired stress was 60MPa.
2.3 Measured results and discussion
Defined applied stress σ and axial strain ε are as follows
A
F=σ (1)
0H
H∆=ε (2)
Where: F is the applied force acting on the specimen surface (N), A is the area of section of the specimen (mm
2), H0 is the initial height of the specimen (mm),
and ∆H is the displacement of the specimen (mm).
peanut, soybean, sesame and linseed compressed at thirteen stresses. Over 50 per cent strain for extruded soybean, sesame and linseed occurred at stress below 20MPa. Over 60 per cent strain for extruded peanut occurred at stress below 10MPa. No more than 13 per cent strain occurred at stress over 20MPa for extruded soybean sesame and linseed, and no more than 13 per cent strain occurred at stress over 10MPa for extruded peanut.
Prior to applying pressure, the specimen is a loose bed owing to a lots of pore space within oilseed specimen. After applying pressure on the specimen, pore space is rapidly dwindled due to gas vented rapidly and elastic deformation in the bed along with increasing pressing pressure. That is why the strains vary sharply at early stage for extruded oilseeds. The bed of oilseeds becomes dense due to plastic deformation. After that the bed becomes a fluid-solid coupling material owing to the cell wall of oilseed and granule broken. Last, the bed becomes oilseed cake as result of bond between broken oilseeds granule. The cake becomes denser and denser as oil is expelled. It explains the reason that no more than 13 per cent strain occurred at later stage for extruded oilseeds.
of room temperature Four series of experiments were carried out under 18°C
Table 1 shows the measured results of strain with stress for extruded
Xiao Zheng et al.
Measurement and Prediction of Stress-Strain for Extruded Oilseed 5
Table 1. Variation of axial strain ε (%) with stress σ (MPa) for the extruded peanut, soybean, sesame and linseed
Stress (MPa) oilseed
0 5 10 15 20 25 30 35 40 45 50 55 60
peanut
soybean
sesame
linseed
0 53.05 61.24 64.14 66.22 68.02 69.42 70.39 71.26 72.04 72.97 73.63 74.49
0 27.54 40.83 46.74 50.50 52.98 55.16 56.10 56.71 57.38 57.98 58.44 59.02
0 39.39 54.34 60.54 63.90 67.65 70.06 72.07 73.68 74.63 75.31 75.95 76.69
0 29.08 44.75 52.71 58.51 62.74 65.40 66.71 67.95 69.06 70.15 70.72 71.26
3. NEURAL NETWORKS IDENTIFICATION
ALGORITHM
It had been proved in theory that feed-forward neural networks trained with the back propagation (BP) can approximate continuous function and curve with arbitrary precision. The BP algorithm is a training learning process, which is divided into two processes, called forward-propagation and back-propagation respectively. Forward propagation is that input data from input layers are transmitted into hidden layer and into output layers after treated by hidden layers and output layers. If the practical output of neural networks is not expected output, the error between practical output and expected output will return through original path to change weights between
of artificial neural networks is actually one process of identification. So, BP neural network have been widely used in system identification to identify
experiment indicated that stress-strain relationship for oilseeds during pressing was nonlinear. In this study, neural networks modeling techniques
the network model, which have r-inputs and one hidden layer.
deviation matrix of the input layer, F1 is the active function of the hidden layer, A1is the output matrix of the hidden layer, W2 is the weight matrix of
are repeated until the prescribed error is met. The training learning process layers, that is back-propagation. Forward propagation and back propagation
complex nonlinear system (Yang Jian et al., 2006; Sun Tao et al., 2005). The
with BP network was used to predict the stress-strain relationship. Fig. 2 is
Fig. 2. Neural network model
P is input matrix, W1 is the weight matrix of the input layer, B1 is the
6 the output layer, B2 is the deviation matrix of the output layer, F2 is the active function of the output layer, and A2 is the output matrix of the output layer.
3.1 Forward transfer of information
The ith node output for hidden layer is
1,,2,1,)11(111
sibPwfar
j
ijiji ⋅⋅⋅=+= ∑=
(3)
Where: a1i is the ith node output of the hidden layer, f1(.)is the active function of the hidden layer, w1ij is the connection weight from the jth input node to the ith hidden node, Pj is the jth input, and b1i is the ith node bias value of the hidden layer.
The kth node output for output layer is
∑=
⋅⋅⋅=+=1
1
2,,2,1),212(22s
i
kikik skbawfa (4)
k
function of the out layer, w2ki is the connection weight from the ith output node of the hidden layer to the kth output node of the output layer, and b2k is the kth node bias value of the output layer.
Adopting the error function as follows
∑=
−=2
1
2)2(2
1),(
s
k
kk atBWE
(5)
Where: E(W,B) is the error function of the output, tk is the kth node objective value of the output layer, and a2k is the kth node output of the output layer.
3.2 Change weight using gradient descent algorithm
The weight from ith input to kth output is
ikiikk
ki
k
kki
ki
aafat
w
a
a
E
w
Ew
112)2(
2
2
222
ηδη
ηη
=−=
∂
∂⋅
∂
∂−=
∂
∂−=∆
(6)
Where: 2kiw∆ is the change in weight of the output layer, η is the learning
rate, 2'f is the active function derivative of the output layer,
'' 22)2( fefat kkkki =−=δ , kkk ate 2−= , where kiδ is the error from the
ith output node of the hidden layer to the kth output node of the output layer,
and ke is the kth output error of the output layer. In the same way
Xiao Zheng et al.
Where: a2 is the kth node output of the output layer, f2(.) is the active
'
Measurement and Prediction of Stress-Strain for Extruded Oilseed 7
kikk
ki
k
kki
ki
fat
b
a
a
E
b
Eb
ηδη
ηη
=−=
∂
∂⋅
∂
∂−=
∂
∂−=∆
'2)2(
2
2
222
(7)
Where: 2kb∆ is the change of the kth node bias value of the output layer.
The weight from jth input to ith output is
ij
i
i
k
kij
ijw
a
a
a
a
E
w
Ew
1
1
1
2
211
∂
∂⋅
∂
∂⋅
∂
∂−=
∂
∂−=∆ ηη
∑=
=−=2
1
'
1' 122)2(
s
k
jijjkkk ppfwfat ηδη (8)
Where: ∆w1ij is the weight change of the hidden layer, f1 is the active
∑=
==2
1
,2,'1s
k
kikiiiij wefe δδ
'2fekki =δ , kkk ate 2−= , where: ijδ
the input layer to the ith output node of the hidden layer, and ie is the ith
node output error of the hidden layer. In the same way
ijib ηδ=∆ 1 (9)
Where: 1ib∆ is the bias value change of the hidden layer. A three layer feed-forward neural networks trained with the back
propagation (BP) algorithm was adopted in this paper. Both input layer and output layer had one node, which represented applied pressures sequence and measured strains sequence respectively. Hidden layer had five nodes. 0.01 and 1000 were used as the error tolerance and the maximum number of
training cycle respectively. Sigmoid function 1)1()(1 −−+= sesf was selected
The measured results had been taken as samples. 11 and 2 data were chosen randomly as training and testing sample respectively. The error function is
( )∑=
−=11
1
22
2
1
k
kk atE (10)
3.3 Results and discussion
shows the curves of relationship between training times and error for peanut, soybean, sesame and linseed during the training process. The values of sum errors of the prediction for peanut, soybean, sesame and linseed were 0.00282, 0.0083, 0.0084 and 0.0047 respectively. The training times were 11,
as active function f1(s). Linear function was selected as active function f 2(s).
Fig. 3 shows the curves of stress-strain predicted and measured, and Fig. 4
'
is the error from the jth input node of
function derivative of the hidden layer,
8
22, 88, and 43 respectively. It was found that there was a lack smooth for prediction curves for oilseeds due to over-fitting when the error tolerance is less than 0.001.
4.
Apparatus and procedures were developed to measure the stress-strain relationships for extruded peanut, soybean, sesame and linseed. Stress-strain
Xiao Zheng et al.
CONCLUSIONS
Fig. 3. Comparison of prediction with measurement
Fig. 4. Relationship between training times and error
(a) peanut (b) soybean
(c) sesame (d) linseed
Measurement and Prediction of Stress-Strain for Extruded Oilseed 9
relationships were measured compressed at twelve pressures (5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60MPa) under uniaxial cold pressing. The model was developed to predict the stress-strain relationship for extruded oilseeds based on BP neural network.
Results indicated that the stress-strain relationships were nonlinear. Over 50 per cent strains for extruded soybean, sesame and linseed occurred at stress below 20MPa. Over 60 per cent strain for extruded peanut occurred at stress below 10MPa. No more than 13 per cent strain occurred at stress over 20MPa for extruded soybean sesame and linseed, and no more than 13 per cent strain occurred at stress over 10MPa for extruded peanut. There were
oilseeds, and there were no significant increases in the values for the strains at later stage for extruded oilseeds.
BP neural network can be used to predict the stress-strain relationship for oilseeds, which not only overcomes the difficulty for theoretical model development, but also avoids requiring considerable technique and experience for nonlinear regression analysis. No more than 0.0084 maximum error showed that the model predicted the stress-strain relationships with highly accuracy. In view of the predicted results and the simple model consisting of input and output layer with one node, and hidden layer with five nodes, the method of stress-strain prediction for oilseeds by using artificial neural networks is both feasible and effective.
ACKNOWLEDGEMENTS
Funding for this research was provided by Hubei Provincial Department of Education (P. R. China). The first author is grateful to the Wuhan Polytechnic University for providing him with pursuing a PhD degree at the Wuhan University of Technology.
REFERENCES
C. R. Sukumaran, B. P. N. Singh. Compression of bed of rapeseeds: the oil-point, Journal of
E. Davion, F. J. Middendof, W. K. Bilanski. Mechanical properties of rapeseed, Canadian
Agricultural Engineering, 1975, 17(1):50-53
Oilseeds. Journal of Agricultural Engineering Research, 1985, 31:361-370
significant increases in the values for the strains at early stage for extruded
Agricultural Engineering Research, 1989, 42:77-84
E. Davion, A. G. Meiering, F. J. Middendof. A theoretical stress model of rapeseed, Canadian
Agricultural Engineering, 1979, 21(1):45-46
G. C. Mrema, P. B. Mcnulty. Mathematical Model of Mechanical Oil Expression from
10 H. J. Rasehom, H. D. Deicke, Xin Yaonian. Theory and praxis of decortication and cold
pressing of rape seed, China oils and fats, 2000, 25(6): 50-54 (in Chinese)
Extruded Soybean Samples. Journal of the American oil chemists society, 1999,
76(2):223-229
P. C. Bargale, Jaswant Singh. Oil expression characteristics of rapeseed for a small capacity
screw press, Journal of food Science Technolage, 2000, 37(2):130-134
P. C. Bargale, R. Ford, D. Jwulfsohn, et al. Measurement of consolidation and permeability
properties of extruded soy under mechanical pressing, Journal of Agricultural Engineering
Sun Tao, Cao Guangyi, Zhu Xinjian. Nonlinear modeling of PEMFC based on neural
networks identification, Journal of Zhejiang University Science,
Yang Jian, Xu Bing, Yang Huayong. Noise identification for hydraulic axial piston pump
based on artificial neural networks, Chinese Journal of mechanical engineering, 2006,
19(1):120-123
Zheng Xiao, Wan nong, Lin Guoxiang, et al. Research on microstructure of cold pressed
cakes from decorticated rapeseed based on porosity, China Oils and Fats, 2004, 29(12):14-
Zheng Xiao, Zeng Shan, Lin Guoxiang, et al. Research on stress-strain of rapeseed and
decorticated rapeseed by uniaxial cold pressing under single surface for flow of fluids
Xiao Zheng et al.
P. C. Bargale, R. J. Ford, F. W. Sosulski, et al. J Irudayaj. Mechanical Oil Expression from
Research, 1999, 74:155-165
Vol. 64, No. 5,
2005, 64(5):365-370
17 (in Chinese)
through a porous medium, China oils and fats, 2004, 29(7):11-14 (in Chinese)
PARAMETERIZED COMPUTER AIDED DESIGN OF STUBBLE CLEANER
Abstract:
which is difficult to modify, and also not intuitive in the solid way for the form
and structure. Therefore, software, UG-NX3, used to conduct
parameterized design of stubble cleaner parts. The parts were designed
associatively and assembled virtually. The structure of the whole machine and
the spatial distribution of parts can be seen and analyzed intuitively. Under
UG-NX3 circumstance, the designed 3D model can be transformed
automatically to 2D drafting which is used in fabrication and production. The
result proved that computer aided parameterized design can allow dynamical
operation, preview and repeated modification of the design, reliably and
quickly. Therefore, the optimum design efficiency of stubble cleaner is
improved; 3D modeling time and 2D drafting time is greatly decreased.
Stubble cleaner, computer aided design, parameterized design
1. INTRODUCTION
In recent years, with the rapid development of computer technology, 3D design and virtual assembling technology were introduced into the mechanical design field, and as a result, product update frequency and
also gradually incorporated into the agricultural machine design field (Yang
Lige Wen2,1, Jianqiao Li
,*1, Xiuzhi Zhang
2, Benard Chirende
1
1 The Key Laboratory for Terrain-Machine Bionics Engineering, Ministry of Education, Jilin
2
* Corresponding author, Address: No. 5988, Renmin Street, Changchun, P. R. China, 130025,
Tel: +86-431-85095760-8407, Fax: +86-431-85095575, Email: [email protected]
Processing, Volume 258; Computer and Computing Technologies in Agriculture, Vol. 1; Daoliang Li;
(Boston: Springer), pp. 11–18.
University,Changchun, China, 130025
The traditional agricultural machine design were represented as 2D drawing,
College of Mechanical Science and Engineering, Jilin University, Changchun, China, 130025
was
Keywords:
Wen, L., Li, J., Zhang, X. and Chirende, B., 2008, in IFIP International Federation for Information
design efficiency were greatly increased (Wen, 2003). These machines are
12 et al., 2004; Yang et al., 2002; Yuan et al., 2006). Computer aided design will be the necessary trend of agriculture machine design field (Yan et al., 2004). Prior to using the virtual assembling technology, design for stubble cleaner was mainly 2D design in which the structure of parts and the whole machine could not be easily visualized, hence associative relationship among parts could be hardily established. In addition, parts assembly and interference checkup could not be conducted. In order to resolve the above problems, parameterized design and virtual assembly were used to design stubble machines based on UG-NX3 (Fu, 2005; Zhao et al., 2005), and this is in line with the trend of stubble cleaner development (Wu et al., 2000).
2. PARAMETERIZED DESIGN AND ASSOCIATIVE
DESIGN
2.1 Parameterized design
Parameterized design method is a new kind of 3D design method, and UG- NX3 is one of the representational 3D parameterized design software. There is driving parameter and calculation parameter under the environment of UG-NX3. Driving parameter means that a variable can be evaluated and its value can be changed at will, whilst calculation parameter is the parameter obtained through calculation based on driving parameter. UG-NX3 has strong sketch functions, utilizing dimension constraint and geometry constraint to drive the sketch, dimensions and shapes of the sketch alter with respect to the change of constraints. Design modification can be done repeatedly and quickly, and the time spent on 3D modeling is markedly reduced.
2.2 Associative design
Associative design means setting up associative relationship among parts such that the associative parts change their sizes and structures simultaneously. Associative design method avoids interference among parts, and makes modifying work convenient and accurate. UG-NX3 provides two assembly methods, one is Bottom-Up design method and the other is Top-Down design method. The former first establishes models of all parts, and then assembles them. Associative constraint must be set up. In contrast, the latter sets up main parts first and other parts come into being according to
Lige Wen et al.
Parameterized Computer Aided Design of Stubble Cleaner 13
Geometry Linker in UG-NX3 serves the Top-Down design method. Parameter modeling establishes interrelated relationships within a part, while WAVE Geometry Linker extends this notion to set up associative relationship among different parts.
3.
3.1 Parts of stubble cleaner
The main function of stubble cleaner is cutting the crop stubble into small pieces and mixing them with earth uniformly and rotarily ridging the earth. Stubble cleaner mainly consists of four components.
� Frame, which supports the whole machine and joins with power framework, and other parts assembled on it.
� Gear-box, which changes the power transmission direction and speed of motion, it is composed of gears with straight tooth, transmission shaft and axletree seat.
� Stubble roller, which cuts up the stubble and ploughs up the earth, it consists of blade tray, stubble-cutting blade, square shaft and axletree seat.
� Shield, whose function is to break up the earth block, ridge the earth, and mix the earth and the stubble.
Next step is to determine main parts of every component, and to make clear the associative relationship among parts.
3.2 3D design of parts
Part modeling process is as shown in Figure 1. Firstly, the main part of
instantiation is ensured; finally, feature-based 3D parameterized model of parts of stubble cleaner can be obtained.
OF STUBBLE CLEANER
PARAMETERIZED DESIGN FOR PARTS
structure is analyzed, sketch is
associative design to other dimensions is conducted and then feature
every component is chosen; next,constructed, driving dimension and calculating dimension are given; then
their associative relationship with the main parts and the dimensions of the main parts. The latter suits people’s design habit very well and avoids assembling interposition, therefore improving design efficiency. WAVE
