Y. Chen

Illinois Institute of Technology

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Y. Zhang

United States Food and Drug Administration

J.-Y. Han

Eye & ENT Hospital of Fudan University

Andrew Howard

Illinois Institute of Technology

Jianping Han

Chinese Academy of Medical Sciences & Peking Union Medical College

Tengchuan Jin

University of Science and Technology of China

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Illinois Institute of Technology

Southern Illinois University Carbondale

United States Food and Drug Administration

Institute for Food Safety and Health

Center for Food Safety and Applied Nutrition

Eye & ENT Hospital of Fudan University

Hanyang University Seoul Hospital

Hanyang University

Entry Exit Inspection and Quarantine Bureau

Walter Reed National Military Medical Center

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Algorithm-based fault tolerance in computation of power flow

Y. Chen Jianping Han

The LU decomposition followed by forward/backward substitution is a very powerful technique for power flow studies. In order to ensure the reliability of computation, the algorithm-based fault tolerance (ABFT) is applied to LU decomposition in power flow studies. This technique is proposed not only to detect and correct errors caused by hardware failure but also to debug programs. Since the ABFT often suffers from roundoff errors when applied to the floating-point number system, a new technique called significant-bit maintenance arithmetic (SBMA) is also suggested for handling numerical problems.< >

Software fault tolerance

10.1109/mwscas.1990.140674

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Adaptive neural network for pattern recognition of 2-D image under affine transformation

Y. Chen J.-Y. Han

Presents a method for recognition of a 2-D image under affine transformation, which can be used as the preprocessing unit in an adaptive neural network. The affine transformation is decomposed into six basic transformations: x-direction and y-direction translations, rotation, x-direction and y-direction expansions (or compressions), and x-direction shear. In order to recognize a 2-D image under affine transformation, the authors introduce the normalized form of an image and design the normalizer by which one can transform an arbitrary input image to its normalized form. The recognition of the images can be done by comparing their normalized forms with a neural network identifier. Some experimental results are reported.< >

10.1109/mwscas.1991.252164

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Structure of peanut major allergen Ara h 3

Journal of Allergy and Clinical Immunology (2009)

Y. Zhang Tengchuan Jin Andrew Howard Y. Chen

10.1016/j.jaci.2008.12.877

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