http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Study on One-Dimensional Wood Board Cutting Stock Problem Based on Adaptive Genetic Algorithm
Wenshu Lin,Dan Mu,Jinzhuo Wu 보안공학연구지원센터 2016 International Journal of Future Generation Communi Vol.9 No.4
When making wood board production, defects on board will influence the machining process automation degree. Therefore, how to fast, accurately remove of wood defects and realize optimal combination cutting stock problem has always been a research hotspot in the field of wood processing. According to the decayed wood board, the paper designed the one dimensional optimization cutting stock combined scheme and mathematical model, adopted the adaptive genetic algorithm imitating the biology evolution to code some optimization scheme initialized by chance, and improved these schemes by selection, crossover and mutation operation. At last these schemes converged to the optimum. The results showed that the adaptive genetic algorithm can achieve a good one dimensional wood board optimization cutting stock problem, through the realization of genetic algorithm in MATLAB, and makes the wood board utilization rate reached 98.9%.
Wenshu Lin,Haokai Xu,Jinzhuo Wu 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.1
To remove the impact of noise on the ultrasonic testing signals of standing trees, wavelet transform method was used to eliminate the noise in the collected ultrasonic signals in the field. In order to achieve the best denoising effect, four kinds of wavelet base denoising parameters including Daubechies (db), Symlets (sym), Coiflets (coif), and Discrete Meyer (dmey) were compared, and the best denoising effect was obtained with db3 wavelet base. The variations of denoising parameters corresponding to the number of db3 wavelet decomposition levels (1- 8) were further analyzed and the decomposition level 4 was demonstrated the best. Meanwhile, the effects of wavelet denoising under different threshold states were compared and the hard rigrsure threshold was demonstrated the best. Experimental results showed that the wavelet transform can effectively remove noise hidden in the ultrasonic signal and improve the denoising effect by selecting reasonable parameters, which laid some initial groundwork for efficient extraction of useful information from the ultrasonic signals.