摘要
针对构建城市固废焚烧(municipal solid waste incineration,MSWI)过程剧毒污染物二噁英(dioxin,DXN)排放风险预警模型的样本极为稀少的问题,提出一种基于主动学习机制生成对抗网络(generative adversarial network,GAN)的DXN排放风险预警建模方法.首先,以DXN风险等级作为条件信息使得GAN生成候选虚拟样本;然后,利用基于最大均值差异和多视角可视化分布信息的主动学习机制进行虚拟样本的初筛和评估,以获得期望虚拟样本;最后,基于混合样本构建DXN排放风险预警模型.通过基准数据集和MSWI过程数据集验证了所提方法的有效性.基于主动学习机制GAN的DXN排放风险预警建模方法可以有效解决样本稀少的问题,提高模型精度.
To solve the problem that the samples to construct a risk warning model of dioxin(DXN)emission in municipal solid waste incineration(MSWI)process are extremely scarce,a modeling method of DXN emission risk warning based on generative adversarial network(GAN)with active learning mechanism was proposed.First,the risk level of DXN was added as condition information to GAN,so that the generator generated candidate virtual samples with specified requirement.Then,the active learning mechanism based on maximum mean discrepancy and multi-view visual distribution information was used to evaluate and screen the virtual samples that met the experts'expectations.Finally,the DXN emission risk warning model was constructed based on the mixed samples composed of virtual samples and real samples.The validity and rationality of the proposed method were verified by using benchmark and MSWI process data sets.The proposed modeling method of DXN emission risk warning based on GAN with active learning mechanism can effectively solve the problem of scarce samples and improve the accuracy of the model.
作者
汤健
崔璨麟
夏恒
王丹丹
乔俊飞
TANG Jian;CUI Canlin;XIA Heng;WANG Dandan;QIAO Junfei(Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China;Beijing Laboratory of Smart Environmental Protection,Beijing 100124,China)
出处
《北京工业大学学报》
CAS
CSCD
北大核心
2023年第5期507-522,共16页
Journal of Beijing University of Technology
基金
国家自然科学基金资助项目(62073006)
北京市自然科学基金资助项目(4212032)。
作者简介
汤健(1974-),男,教授,博士生导师,主要从事小样本数据建模、固废处理智能控制方面的研究,E-mail:freeflytang@bjut.edu.cn。
