N,N-二甲基甲酰胺(DMF)作为有机溶剂在化工领域得到了广泛的应用。本研究通过对小试升流式厌氧污泥床(UASB)反应器处理高浓度DMF工业废水的实验进行评估。实验中,UASB在有限有机负荷率(OLR)范围1.57~9.59 kg COD/(m^(3)·d)下运行,...N,N-二甲基甲酰胺(DMF)作为有机溶剂在化工领域得到了广泛的应用。本研究通过对小试升流式厌氧污泥床(UASB)反应器处理高浓度DMF工业废水的实验进行评估。实验中,UASB在有限有机负荷率(OLR)范围1.57~9.59 kg COD/(m^(3)·d)下运行,将水力停留时间(HRT)由48 h缩短至8 h,去除效率优良,达到95%以上。利用经验方程对DMF废水处理过程的CO_(2)排放量和生物能源产生量进行了估算和评估,相比传统的活性污泥(CAS)工艺,UASB工艺具有明显优势,能够回收能源并减少CO_(2)排放,其正净能量潜力为0.061 kW·h/m^(3),同时还显著减少了6.18 kg/m^(3)的CO_(2)排放量。本研究的结果证明了UASB工艺在处理DMF废水方面的潜力和可行性,并促进了碳中和理念在废水处理中的推广。展开更多
This paper proposes an artificial intelligence-based robust information hiding algorithm to address the issue of confidential information being susceptible to noise attacks during transmission.The algorithm we designe...This paper proposes an artificial intelligence-based robust information hiding algorithm to address the issue of confidential information being susceptible to noise attacks during transmission.The algorithm we designed aims to mitigate the impact of various noise attacks on the integrity of secret information during transmission.The method we propose involves encoding secret images into stylized encrypted images and applies adversarial transfer to both the style and content features of the original and embedded data.This process effectively enhances the concealment and imperceptibility of confidential information,thereby improving the security of such information during transmission and reducing security risks.Furthermore,we have designed a specialized attack layer to simulate real-world attacks and common noise scenarios encountered in practical environments.Through adversarial training,the algorithm is strengthened to enhance its resilience against attacks and overall robustness,ensuring better protection against potential threats.Experimental results demonstrate that our proposed algorithm successfully enhances the concealment and unknowability of secret information while maintaining embedding capacity.Additionally,it ensures the quality and fidelity of the stego image.The method we propose not only improves the security and robustness of information hiding technology but also holds practical application value in protecting sensitive data and ensuring the invisibility of confidential information.展开更多
基金the National Natural Science Foundation of China(Nos.62272478,61872384)Natural Science Foundation of Shanxi Province(No.2023-JC-YB-584)+1 种基金National Natural Science Foundation of China(No.62172436)Engineering University of PAP’s Funding for Scientific Research Innovation Team,Engineering University of PAP’s Funding for Key Researcher(No.KYGG202011).
文摘This paper proposes an artificial intelligence-based robust information hiding algorithm to address the issue of confidential information being susceptible to noise attacks during transmission.The algorithm we designed aims to mitigate the impact of various noise attacks on the integrity of secret information during transmission.The method we propose involves encoding secret images into stylized encrypted images and applies adversarial transfer to both the style and content features of the original and embedded data.This process effectively enhances the concealment and imperceptibility of confidential information,thereby improving the security of such information during transmission and reducing security risks.Furthermore,we have designed a specialized attack layer to simulate real-world attacks and common noise scenarios encountered in practical environments.Through adversarial training,the algorithm is strengthened to enhance its resilience against attacks and overall robustness,ensuring better protection against potential threats.Experimental results demonstrate that our proposed algorithm successfully enhances the concealment and unknowability of secret information while maintaining embedding capacity.Additionally,it ensures the quality and fidelity of the stego image.The method we propose not only improves the security and robustness of information hiding technology but also holds practical application value in protecting sensitive data and ensuring the invisibility of confidential information.
