Dr. Bin Wan Profile

Dr. Bin Wan

at Wuhan Univ

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Publications (2)

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Proceedings Article | 27 September 2016 Paper

Development of glucose measurement system based on pulsed laser-induced ultrasonic method

Zhong Ren, Bin Wan, Guodong Liu, Zhihua Xiong

Proceedings Volume 9684, 96842G (2016) https://doi.org/10.1117/12.2241490

KEYWORDS: Glucose, Pulsed laser operation, Ultrasonics, Photoacoustic spectroscopy, Transducers, Signal detection, Optical parametric oscillators, Light sources, Nd:YAG lasers, Neodymium

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In this study, a kind of glucose measurement system based on pulsed-induced ultrasonic technique was established. In this system, the lateral detection mode was used, the Nd: YAG pumped optical parametric oscillator (OPO) pulsed laser was used as the excitation source, the high sensitivity ultrasonic transducer was used as the signal detector to capture the photoacoustic signals of the glucose. In the experiments, the real-time photoacoustic signals of glucose aqueous solutions with different concentrations were captured by ultrasonic transducer and digital oscilloscope. Moreover, the photoacoustic peak-to-peak values were gotten in the wavelength range from 1300nm to 2300nm. The characteristic absorption wavelengths of glucose were determined via the difference spectral method and second derivative method. In addition, the prediction models of predicting glucose concentrations were established via the multivariable linear regression algorithm and the optimal prediction model of corresponding optimal wavelengths. Results showed that the performance of the glucose system based on the pulsed-induced ultrasonic detection method was feasible. Therefore, the measurement scheme and prediction model have some potential value in the fields of non-invasive monitoring the concentration of the glucose gradient, especially in the food safety and biomedical fields.

Proceedings Article | 16 October 2009 Paper

Construction and application of particle swarm optimization algorithm for ecological spatial data mining

ZhongLiang Fu, Bin Wan

Proceedings Volume 7492, 74921Y (2009) https://doi.org/10.1117/12.838457

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The research of the regional ecological environment becomes more important to regional Sustainable Development in order to achieve the harmonious relationship between the person and the nature. The advent of spatial information technologies, such as GIS, GPS and RS, have great enhanced our capabilities to collect and capture spatial data. How to discover potentially useful information and knowledge from massive amounts of spatial data is becoming a crucial project for spatial analysis and spatial decision making. Particle Swarm Optimization has a powerful ability for reasoning and semantic representation, which combined with qualitative analysis and quantitative analysis, with prior knowledge and observed data, and provides an effective way to spatial data mining. This paper focuses on construction and learning a Particle Swarm Optimization model for spatial data mining. Firstly, the theory of spatial data mining is introduced and the characteristics of Particle Swarm Optimization are discussed. A framework and process of spatial data mining is proposed. Then we construct a Particle Swarm Optimization model for spatial data mining with the given dataset. The research area is focused on the distribution of pollution sources in Wuhan City. The experimental results demonstrate the feasibility and practical of the proposed approach to spatial data mining. Finally, draw a conclusion and show further avenues for research. Through the empirical study, it has been proved that Particle Swarm Optimization algorithm is feasible and the conclusion can provide instruction for local environmental planning.

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