Papers by Author: Xiao Ming Yang

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Authors: Xiao Ming Yang
Abstract: DDFS is widely used in digital signal processing and communications. Orthogonal sine wave oscillator is the main part of DDFS. In this paper, a new CORDIC algorithm implemented based on FPGA is proposed to realize DDFS. Replace the traditional look-up table ROM method to CORDIC algorithm. The algorithm has been realized in the FPGA using the pipeline architecture. The design has the advantages of little hardware resource consumption, high accuracy, and without memory. At the end, give a simulation waveform and spectrum analysis by AD converter, show the result in oscilloscope.
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Authors: Mei Zhou, Xiao Ming Yang, Kun Song
Abstract: this paper got the compressive stress-strain fitting curve of crumb rubber plastic concrete, analyzed the influencing factors of elastic modulus of crumb rubber plastic concrete and found the influence law of specimen size and measuring distance to measured value of elastic modulus, through our experiments. The results of our experiments show that the method and operation of experiment strongly influence the measured value of elastic modulus of crumb rubber plastic concrete, but we can use the measures of step loading, screening and fitting curve to get the relatively exact measured value of elastic modulus of plastic concrete. And then, this paper analyzed a 16 sets of uniform experiment results with regression analysis method. As a result, we found the main influencing factors of elastic modulus of crumb rubber concrete are cement mixing amount, crumb rubber mixing amount and product of water-binder ratio and water reducer mixing amount, which the importance of them decreases progressively. Finally, this paper established the prediction equation of elastic modulus of crumb rubber concrete and found the connection of elastic modulus of crumb rubber concrete with specimen size and measuring distance.
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Authors: Xiao Ming Yang, Dan Shi
Abstract: Recently, the safety of existing civil engineering structures attracts more and more attention. The long-term strength of concrete plays a key role during the assessment of safety and durability for civil engineering structures. The strength of concrete will gradually decrease during the service of civil engineering structures. It is significant to accurately predict the strength deterioration of concrete for correctly evaluating the safety of structures. The factors affecting the long-term strength of concrete include environment type, age, climate, water cement ratio, amount of cementing material and so on. In this paper, artificial neural network with powerful mapping ability has been selected to predict the long-term strength of concrete. First, there-layer BP neural network with age, water cement ratio, amount of cementing material as input and long-term strength as output was built. Then, the neural network was trained by the samples measured in real structures and the well-trained neural network was test. From the test results, the trained neural network can accurately predict the long-term strength of concrete with the error less then 9%.
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