应义斌
主要从事生物图像和计算机视觉技术、基于近红外成像和光谱分析技术的生物物料品质无损检测、农产品品质实时智能化检测和分级机器人技术、车辆自动导航技术及工厂化农业装备等方面的科研和教学工作。
个性化签名
- 姓名:应义斌
- 目前身份:
- 担任导师情况:
- 学位:
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学术头衔:
博士生导师, 教育部“新世纪优秀人才支持计划”入选者
- 职称:-
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学科领域:
农业工程
- 研究兴趣:主要从事生物图像和计算机视觉技术、基于近红外成像和光谱分析技术的生物物料品质无损检测、农产品品质实时智能化检测和分级机器人技术、车辆自动导航技术及工厂化农业装备等方面的科研和教学工作。
邓辉舫(DENG, Huifang),英籍华人,教授,博士生导师,教育部认证的高层次海外留学人才,1957年10月出生于湖南。分别于1981年及1985年在中国获得理学学士及理学硕士学位,于2000年在英国伦敦大学学院(UCL)获得理学博士学位。博士论文是2D液晶显示(LCD)系统关键技术研究(计算机模拟by有限元方法)。
由于他在理论凝聚态(理论物理)方面的出色研究,于1987年被破格提升为副教授,是年29岁。从1989年1月起,在英国学习,工作与生活。
从2001年1月到2004年9月,作为首席科学家及技术总监受聘于英国硅谷-剑桥一家高科技工程软件公司。以其深厚的数学功底及超常的算法分析能力使公司产品的功能及性能跃升到了一个至高的新层次,曾3次获得公司总裁颁发的杰出表现奖。
2004年9月,辞职受聘回国担任华南理工大学国家示范性软件学院院长。现为华南理工大学计算机科学与工程学院教授,博士生导师,先进计算、服务计算与科学计算团队负责人。
发表论文:
到目前为止共发表论文60多篇,其中近40篇在国外刊物上发表,7篇在国际一流刊物上发表(美国物理评论B (Phys. Rev. B)5篇,IEEE Trans 2篇),30篇被SCI,EI,ISTP索引,单篇最高引用次数为 16次。
科研项目:
在英国共主持大中型项目6项。其中包括欧共体项目:Predictive 3D micro-model simulation for Monitor LCDs。
回国后(2004.9)所进行的科研项目:1)用户状态管理子系统开发(企业) ;2)2006年广东省科技厅粤港关键领域重点突破招标立项:“无线射频识别(RFID)物流通关公共服务平台关键技术”(项目编号2006A15006003);3)2006年国家“863”先进制造技术领域RFID技术与应用重点项目:“面向物流应用的电子标签(RFID)服务系统研究”(课题编号:2006AA04A120);4)2007年教育部资助直属高校聘请外籍教师重点项目。
社会兼职:
英国剑桥旭日软件公司客座首席科学家;亚太地区ICT年度大奖赛特邀独立国际评委;2008年度《国家自然科学奖》特邀评审专家(英文组);2006年度863计划信息领域虚拟现实专题课题评议专家;2006年度和2008年度863计划信息技术领域智能感知与先进计算技术专题课题评议专家;IFITA2009 - 国际信息技术与应用论坛)审稿专家组成员;(美国) 国际制造技术与管理杂志(IJMTM)审稿人;广东省企业管理现代化成果评审委员会委员;粤港RFID产业联盟副主席;【大型文献馆藏传书】《当代湖南人-杰出人物篇》编纂委员会委员,并被录入该传书《杰出人物篇》;《中国科技论文在线》特聘评审专家。
个人主页:http://202.38.194.240:8180/CSWeb/showTeacher.html?id=48
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【期刊论文】Use of FT-NIR spectrometry in non-invasive measurements of internal quality of 'Fuji' apples
应义斌, Yande Liu a, b, Yibin Ying a, ∗
Postharvest Biology and Technology xxx (2005) xxx-xxx,-0001,():
-1年11月30日
This research studied the feasibility of making rapid measurements of the soluble solids contents (SSC) and acidity of 'Fuji'apple (Malus domestica Borkh. cv. Fuji) fruit. FT-NIR spectra were recorded in the interactance mode, using fiber optics and a special sample holder. Calibration models related the FT-NIR spectra to SSC, titratable acidity (TA) and available acidity (pH) were developed based on partial least square (PLS) regression with respect to the logarithms of the reflectance reciprocal and its first and second derivative. The prediction performance of calibration models in different wavelength regions was also investigated. The best models gave a standard errors of prediction (SEP) of 0.455, 0.044 and 0.068, and correlation coefficients of 0.968, 0.728 and 0.831 for SSC, TA and pH, respectively, in the wavelength range of 812-2357 nm. Based on the results, it was concluded that FT-NIR spectrometry could be easy to facilitate, reliable, accurate and fast method for non-invasive measurements of apple SSC and acidity.
FT-NIR spectrometry, Non-invasive measurements, Apples, Fruit quality, PLS
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应义斌, LIU Yan-de (刘燕德), , YING Yi-bin (应义斌)†, FU Xia-ping (傅霞萍)
J Zhejiang Univ SCI 2005 6B (3): 158-164,-0001,():
-1年11月30日
To develop nondestructive acidity prediction for intact Fuji apples, the potential of Fourier transform near infrared (FT-NIR) method with fiber optics in interactance mode was investigated. Interactance in the 800nm to 2619nm region was measured for intact apples, harvested from early to late maturity stages. Spectral data were analyzed by two multivariate calibration techniques including partial least squares (PLS) and principal component regression (PCR) methods. A total of 120 Fuji apples were tested and 80 of them were used to form a calibration data set. The influences of different data preprocessing and spectra treatments were also quantified. Calibration models based on smoothing spectra were slightly worse than that based on derivative spectra, and the best result was obtained when the segment length was 5 nm and the gap size was 10 points. Depending on data preprocessing and PLS method, the best prediction model yielded correlation coefficient of determination (r2) of 0.759, low root mean square error of prediction (RMSEP) of 0.0677, low root mean square error of calibration (RMSEC) of 0.0562. The results indicated the feasibility of FT-NIR spectral analysis for predicting apple valid acidity in a nondestructive way.
Apples,, Nondestructive prediction,, FT-NIR,, Valid acidity,, Multivariate analysis
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应义斌, Y. B. Ying, Y. D. Liu, J. P. Wang, X. P. Fu, Y. B. Li
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-1年11月30日
This research evaluated the potential of a Fourier transform near−infrared (FT−NIR) spectrometer with a reflectance fiber optic probe for determination of total soluble solids (TSS) content and available acid (VA) in intact peaches. It also investigated the relative performance of the calibration models with different data preprocessing methods (original, first derivative, and second derivative). Reflectance spectra were collected from two opposite sides of individual peaches, followed by standard destructive methods for analyzing TSS and VA of peaches. Calibration models were developed with the use of a partial least squares (PLS) technique. The best calibration model for TSS gave good predictions of TSS, with a coefficient of determination (r2) of 0.916 and a standard error of prediction (SEP) of 0.534. The best calibration model for VA prediction yielded (r2)=0.904 and SEP=0.129. Thus, FT−NIR reflectance can be used to predict the TSS and VA of intact peaches. Based on the results, it was concluded that FT−NIR spectrometry could be an easy to facilitate, reliable, accurate, and fast method for non−invasive measurement of peach TSS and VA.
Available acid,, FT−NIR,, Peach,, PLS technique,, Total soluble solids content.,
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应义斌, YU Chao-gang (余朝刚), YING Yi-bin (应义斌)†, WANG Jian-ping (王剑平), NOURAIN Jamal, YANG Jia (杨佳)
J Zhejiang Univ SCI 2005 6A (4): 265-269,-0001,():
-1年11月30日
Proportional integral plus feedforward (PI+FF) control was proposed for identifying the pipe temperature in hot water heating greenhouse. To get satisfying control result, ten coefficients must be adjusted properly. The data for training and testing the radial basic function (RBF) neural-networks model of greenhouse were collected in a 1028m2 multi-span glasshouse. Based on this model, a method of coefficients adjustment is described in this article.
PI control,, Greenhouse,, Temperature,, Neural networks
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应义斌, Y. Ying, H. Jing, Y. Tao, N. Zhang
,-0001,():
-1年11月30日
Huanghua pear is an important fruit in China. The shape and condition of the stems are important indices for classifying Huanghua pears. Images of Huanghua pears were acquired with a machine vision system. Using templates with different sizes, an algorithm for judging the presence of stems was developed. Meanwhile, the stem head and the joint point between the stem and the pear body were labeled. After calculating slopes of the approximate tangential lines of the stem at the head and bottom positions, the included angle of these two lines was obtained. It was found that the included angle of a broken stem was smaller than that of a good stem. Based on this feature, good stems can be distinguished from broken stems. Results of a test on 53 pear images showed that the accuracy for judging the presence and integrity of the stems reached 100% and 93%, respectively. A method for describing the irregular shapes of Huanghua pears was also studied. Fourier transformation and Fourier inverse transformation pairs were used as the shape descriptors. The first 16 harmonic components of the Fourier descriptor were found sufficient to represent the primary shapes of uanghua pears. These components were used as the inputs to an artificial neural network (ANN) to classify Huanghua pears. The classification accuracy reached 90%.
Machine vision,, Huanghua pear,, Stem,, Shape,, Classification.,
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