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Your search keyword '"Liang Zhang"' showing total 2 results
2 results on '"Liang Zhang"'

1. A method for in vivo detection of abnormal subepidermal tissues based on dielectric properties

Author

Dongming Zhou, Peiguo Liu, Xuetao Shi, Xiuzhen Dong, and Liang Zhang

Subjects
Materials science, Biomedical Engineering, Reproducibility of Results, Hemorrhage, General Medicine, Dielectric, Microwave frequency, Models, Biological, Sensitivity and Specificity, Skin Diseases, Biomaterials, Coaxial probe, In vivo, Dielectric Spectroscopy, Simulated annealing, Humans, Equivalent circuit, Computer Simulation, Diagnosis, Computer-Assisted, Microwaves, Tomography, Algorithms, Skin, Biomedical engineering
Abstract

In this paper, a convenient and noninvasive scanning imaging method using microwave frequency band for ab- normal subepidermal tissues detection is proposed in the aim to improve diagnosis and prognosis in clinical environments. This method is based on the reflective detection technology with coaxial probe that is used to measure the dielectric proper- ties of tissues. An improved equivalent circuit and simulated annealing algorithm (SA) were used in this work to analyze the dielectric properties of tissues. Computational simulations incorporating a simplified model of subcutaneous hemorrhage described in this work were used to evaluate this method. The dielectric properties data of tissues in the model of simulation is derived from the literature. The simulation results demonstrated the potential of this method to detect abnormal subepider- mal tissues conveniently and expose them in the image accurately.

Published
2014

2. Applying probabilistic latent semantic analysis to multi-criteria recommender system.

Author

Yin Zhang, Yueting Zhuang, Jiangqin Wu, and Liang Zhang

Subjects
STATISTICS, LEARNING, PROBABILITY theory, ALGORITHMS, GAUSSIAN distribution, REGRESSION analysis
Abstract

Nowadays some recommender system researchers have already been engaging multi-criteria that model possible attributes of the item to generate the improved recommendations. However, the statistical machine learning methods successful in the single-rating recommender system have not been investigated in the context of multi-criteria ratings. In this paper, we propose two types of multi-criteria probabilistic latent semantic analysis algorithms extended from the single-rating version. First, the mixture of multi-variate Gaussian distribution is assumed to be the underlying distribution of multi-criteria ratings of each user. Second, we further assume the mixture of the linear Gaussian regression model as the underlying distribution of multi-criteria ratings of each user, inspired by the Bayesian network and linear regression. The experiment results on the Yahoo!Movies ratings data set show that the full multi-variate Gaussian model and the linear Gaussian regression model achieve a stable performance gain over other tested methods. [ABSTRACT FROM AUTHOR]

Published
2009
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