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- 저자 : Dongyu Chen (검색결과 4 건)
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Optimizations of Canny Edge Detection in Ghost Imaging
Guohua Wu,Dongyue Yang,Chen Chang,Longfei Yin,Bin Luo,Hong Guo 한국물리학회 2019 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.75 No.3
An optimization of the Canny edge detector’s application in ghost imaging is presented. Based on the pseudo-thermal light ghost imaging scheme with a binary object, a thin and accurate edge map can be extracted by using a Gaussian-filtering-optimized Canny edge detector. The scale of the Gaussian filter in Canny edge detection algorithm is the dominate factor in the performance of the edge detector, and can be evaluated by the bit error rate of reconstructed binary image based on the edge map. Simulation results indicate the optimal window size of Gaussian filter for ghost imaging is proportional to the full width at half maximum of the self-correlation function in the idler arm samples without any priori knowledge of the object. Experimental results show that, with an appropriate Gaussian filter, the reconstructed binary image can approach the original binary object with the minimum bit error rate, which means the edge detection result is optimal.
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Experimental Study on Residual Mechanical Properties of Bolt-Sphere Joints After a Fire
Hongbo Liu,Zhilun Tan,Zhihua Chen,Zhansheng Liu,Dongyu Liu 한국강구조학회 2018 International Journal of Steel Structures Vol.18 No.3
Through tensile testing of 15 steel bolt-sphere joints and 15 aluminum alloy bolt-sphere joints, infl uencing rules of material type, high temperature and cooling mode on tensile properties of post-fi re bolt-sphere joints were studied, and then failure modes of the joints were determined. According to experimental data, the calculation formula of residual tension capacity of post-fi re bolt-sphere joints was presented. Experimental data indicated that (1) decreased extension on the mechanical properties of aluminum alloy bolt-sphere joints after high temperature in the fi re was signifi cantly higher than that of the steel bolt-sphere joints; (2) when fi re temperature was lower than 800 °C, the tension capacity of steel bolt-sphere joints could be restored by more than 90% after cooling; (3) when fi re temperature was lower than 500 °C, the tension capacity of aluminum alloy bolt-sphere joints could be restored by more than 50% after cooling; (4) bolt-sphere joints could still satisfy the requirement of “strong node and weak member” after a fi re; and (5) material type and fi re temperature were the main factors that infl uenced post-fi re mechanical properties of bolt-sphere joints.
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Tian Ai,Xiaojun Jiang,Hongmei Yu,Hong-Bo Xu,Dawei Pan,Qingyu Liu,Dongyu Chen,Jinyang Li 한국화학공학회 2015 Korean Journal of Chemical Engineering Vol.32 No.2
Two biosorbents were prepared using bamboo powder modified with mercaptoacetic acid and carbondisulfide, which exhibits strong adsorption properties for Cu2+ and Ni2+. The obtained materials were characterized byFTIR. Maximum adsorption for both metals was found to occur around pH 5.0-6.5. The kinetic data followed thepseudo-second-order model. The maximum adsorption capacities of Cu2+ and Ni2+ on mercaptoacetic acid modifiedbamboo powder determined from Langmuir isotherm were 103.97mg g−1 and 61.35mg g−1, respectively. While oncarbon disulfide modified bamboo powder were 128.21mg g−1 and 56.82mg g−1, respectively. The adsorption mechanismanalysis revealed that the most possible adsorption mode of Cu2+ was coordination, and Ni2+ was ion exchange. The obtained adsorbents could effectively remove Cu2+ and Ni2+ from industrial electroplating wastewater and could beused repeatedly for more than five cycles.
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Adversarial Complementary Learning for Just Noticeable Difference Estimation
Dong Yu,Jian Jin,Lili Meng,Zhipeng Chen,Huaxiang Zhang 한국인터넷정보학회 2024 KSII Transactions on Internet and Information Syst Vol.18 No.2
Recently, many unsupervised learning-based models have emerged for Just Noticeable Difference (JND) estimation, demonstrating remarkable improvements in accuracy. However, these models suffer from a significant drawback is that their heavy reliance on handcrafted priors for guidance. This restricts the information for estimating JND simply extracted from regions that are highly related to handcrafted priors, while information from the rest of the regions is disregarded, thus limiting the accuracy of JND estimation. To address such issue, on the one hand, we extract the information for estimating JND in an Adversarial Complementary Learning (ACoL) way and propose an ACoL-JND network to estimate the JND by comprehensively considering the handcrafted priors-related regions and non-related regions. On the other hand, to make the handcrafted priors richer, we take two additional priors that are highly related to JND modeling into account, i.e., Patterned Masking (PM) and Contrast Masking (CM). Experimental results demonstrate that our proposed model outperforms the existing JND models and achieves state-of-the-art performance in both subjective viewing tests and objective metrics assessments.
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