Dioxin emission prediction from a full-scale municipal solid waste incinerator: deep learning model in time-series input
The immeasurability of real-time dioxin emissions is the principal limitation to controlling and
reducing dioxin emissions in municipal solid waste incineration (MSWI). Existing methods …
reducing dioxin emissions in municipal solid waste incineration (MSWI). Existing methods …
城市固废焚烧过程智能优化控制研究现状与展望
汤健， 夏恒， 余文， 乔俊飞 - 自动化学报, 2023 - aas.net.cn
针对全球城市固废(Municipal solid waste, MSW) 的高增长率导致城市环境持续恶化以至于“
垃圾围城” 现象日益增多等问题, MSW 焚烧(MSW incineration, MSWI) 技术能够通过发酵, 燃烧 …
垃圾围城” 现象日益增多等问题, MSW 焚烧(MSW incineration, MSWI) 技术能够通过发酵, 燃烧 …
An Overview of Artificial Intelligence Application for Optimal Control of Municipal Solid Waste Incineration Process
Artificial intelligence (AI) has found widespread application across diverse domains,
including residential life and product manufacturing. Municipal solid waste incineration …
including residential life and product manufacturing. Municipal solid waste incineration …
Prediction of Main parameters of steam in waste incinerators based on BAS-SVM
L Chen, C Wang, R Zhong, Z Li, Z Zhao, Z Zhou - Sustainability, 2023 - mdpi.com
The main steam parameters of a waste-to-energy plant are the key indicator of the safety
and stability of its combustion process. Accurate prediction of the main steam parameters …
and stability of its combustion process. Accurate prediction of the main steam parameters …
Complex integrity constraint discovery: measuring trust in modern intelligent railroad systems
Data are at the heart of intelligent rail systems in the high-speed transportation sector (Zhou
et al., 2020; Ho et al., 2021; Hu et al., 2021; Chen et al., 2022). The core of modern …
et al., 2020; Ho et al., 2021; Hu et al., 2021; Chen et al., 2022). The core of modern …
自回归神经网络的预测值反馈再训练策略及应用.
莫正阳， 李益国 - Journal of Southeast University/Dongnan …, 2024 - search.ebscohost.com
为提高非线性自回归神经网络(NARX-NN) 的多步预测性能, 提出了一种预测值反馈再训练(FR)
策略. 首先采用常规训练策略对NARX-NN 进行训练, 然后利用模型的单步预测结果替换实测值 …
策略. 首先采用常规训练策略对NARX-NN 进行训练, 然后利用模型的单步预测结果替换实测值 …
复杂完整性约束: 在现代智能交通系统中衡量模型可信性
目的: 由于使用的列车运行数据偏离系统应用的数据特征, 现代智能交通系统部署的模型推理
结果可能不可靠. 本文旨在研究部署在系统中的模型使用的数据变化对模型性能的影响 …
结果可能不可靠. 本文旨在研究部署在系统中的模型使用的数据变化对模型性能的影响 …