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Dioxin Emission Concentration Soft Measurement Model of MSWI Process Based on Unmarked Samples and Improved Deep Belief Network

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Dioxin Emission Concentration Soft Measurement Model of MSWI...

第三十九届中国控制会议

作者： zihao Guo Jian tang Junfei Qiao Haijun He Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System

Municipal solid waste incineration（MSWI） is a widely used domestic waste resource treatment technology. However, this process products pollution dioxins（DXN）. It has high toxicity and durability. This is one of the main reasons of "not in my backyard" effect when constructing MSWI plant. As the long period and high cost of off-line DXN detection method, it is difficult to realize the real-time monitoring of DXN emission concentration. The massive unlabeled samples in the industrial field contain the generation mechanism of DXN, which are not fully utilized. Aim at the above problems, this paper proposes an unmarked samples and improved deep belief network（DBN） based method to construct DXN soft measurement model. Firstly, a large number of unlabeled samples are added to the training input sample set to improve the learning ability of soft measurement model at pre training phase. Then, an energy function is derived as the activation function of restricted Boltzmann machine（RBM）. Finally, for the whole DBN, dropout algorithm is used to improve the robustness of the model, and the adaptive learning rate error back propagation algorithm is used to fine tune the weight iteratively at the fine tuning phase. The validity and rationality of this method are validated by DXN data set.

关键词： Municipal solid waste incineration(MSWI) Dioxin(DXN) Soft measurement Deep belief network(DBN)

Dioxin Emission Concentration Soft Measuring Method Based on Selective Ensemble Least Square Support Vector Machine Algorithm

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Dioxin Emission Concentration Soft Measuring Method Based on...

第37届中国控制会议

作者： Jian tang Junfei Qiao zihao Guo Aijun Yan Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System

Dioxin（DXN） is a highly toxic and persistent pollutant discharged from municipal solid waste incineration（MSWI）.The first principal model of DXN is difficult to establish due to the complex physical and chemical characteristics of the incineration process. In the practical process, DXN emission concentration is off-line detected with monthly or seasonal *** at such small sample modeling problem, a soft measuring method based on selective ensemble（SEN） least square support vector machine（LSSVM） for modeling DXN emission concentration is proposed. At first, candidate training sub-samples are produced from original training samples. Then, different candidate sub-sub-models based on the same kernel parameter and regularization parameter are constructed by using LSSVM. Thirdly, ensemble sub-models are selected by using the genetic algorithm optimization tool box and prior knowledge. Finally, these ensemble sub-models are combined by using partial least squares algorithm in terms of reduction con-linearity among different prediction outputs. Simulation results show effectiveness of the proposed approach by using dataset in reference [18].

关键词： Municipal solid waste incineration (MSWI) dioxin (DXN) selective ensemble least square support vector machine soft measuring model

Dark current modeling of thick perovskite X‑ray detectors

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Frontiers of Optoelectronics 2022年第4期15卷 19-29页

作者： Shan Zhao Xinyuan Du Jincong Pang Haodi Wu zihao Song Zhiping Zheng Ling Xu Jiang tang Guangda Niu Wuhan National Laboratory for Optoelectronics and School of Optical and Electronic Information Huazhong University of Science and TechnologyWuhan 430074China Optical Valley Laboratory Wuhan 430074China

Metal halide perovskites(MHPs)have demonstrated excellent performances in detection of X-rays and *** studies focus on improving the sensitivity of single-pixel MHP ***,little work pays attention to the dark current,which is crucial for the back-end circuit ***,the requirement of dark current is quantitatively evaluated as low as 10^(−9)A/cm^(2)for X-ray imagers integrated on pixel ***,through the semiconductor device analysis and simulation,we reveal that the main current compositions of thick perovskite X-ray detectors are the thermionic-emission current(J_(T))and the generation-recombination current(J_(g-r)).The typical observed failures of p-n junctions in thick detectors are caused by the high generation-recombination current due to the band mismatch and interface *** work provides a deep insight into the design of high sensitivity and low dark current perovskite X-ray detectors.

关键词： Perovskite X-ray detection Dark current Semiconductor simulation Junction device

Prediction of MSWI furnace temperature based on TS fuzzy neural network

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Prediction of MSWI furnace temperature based on TS fuzzy neu...

第三十九届中国控制会议

作者： Haijun He Xi Meng Jian tang Junfei Qiao zihao Guo Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System

Furnace temperature is an important indicator to control and optimize the Municipal Solid Wastes Incineration（MSWI） process. However, limited by the environment and instruments, it is difficult to measure the furnace temperature online and accurately. In this paper, a TS fuzzy neural network is utilized to design the prediction model in MSWI process, trying to obtain the real-time and accurate measurement of the furnace temperature. First, the mechanism of the MSWI process is introduced in brief. Then, the structure and training method of the TS-fuzzy-neural-network-based prediction model is introduced in details, which helps to build the nonlinear relationship between the furnace temperature and other process ***, the designed prediction model is applied to a real MSWI plant, and simulation results demonstrate the effectiveness and outperformance of the proposed methodology.

关键词： Municipal solid waste incineration(MSWI) Furnace temperature TS fuzzy neural network prediction

Soft Measuring Method of Dioxin Emission Concentration for MSWI Process Based on RF and GBDT

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Soft Measuring Method of Dioxin Emission Concentration for M...

第32届中国控制与决策会议

作者： Heng Xia Jian tang Junfei Qiao Aijun Yan zihao Guo Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System

Dioxin（DXN） is a highly toxic pollutant emitted during municipal solid waste incinerator（MSWI） *** the actual industrial process,DXN emission concentration is measured through offline experiment analysis,which has shortcomings such as long time and high *** this study,a soft-sensing model of DXN emission concentration was established by using MSWI process *** forest（RF） and gradient boosting decision tree algorithms are used to construct ensemble learning-based DXN ***,RF tree sub-models are constructed base on random sampling and CART regression ***,Gradient boosting decision tree（GBDT） method is used to each RF sub-model,in which one gradient iteration is performed to reduce the prediction ***,a simple average combination strategy is performed on these RF and GBDT based ***,the soft measuring model of DXN emission concentration based on small samples and high-dimensional MSWI process data is *** proposed method can both reduce model variance and eliminate prediction *** experimental results show that the proposed method can further improve the prediction performance and generalization ability.

关键词： Municipal solid waste incinerator(MSWI) Soft measuring model Dioxin(DXN) emission concentration Random forest(RF) Gradient boosting decision tree(GBDT)

基于高清工业相机的盾构隧道裂缝智能识别算法分析

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测绘通报 2021年第8期 83-87,101页

作者：李梓豪唐超北京城建勘测设计研究院有限责任公司北京100101

裂缝一直是隧道病害的重点检测对象,但传统人工巡检仅能通过肉眼发现后记录,人工识别精准度与效率完全取决于个人经验判断,无信息化手段辅助,作业效率识别精度亟待提升。针对以上问题,本文借助高清工业相机成像分辨率高、采集速度快等特... 详细信息

裂缝一直是隧道病害的重点检测对象,但传统人工巡检仅能通过肉眼发现后记录,人工识别精准度与效率完全取决于个人经验判断,无信息化手段辅助,作业效率识别精度亟待提升。针对以上问题,本文借助高清工业相机成像分辨率高、采集速度快等特点,将高清工业相机部署于轨道车上获取隧道表面裂缝病害信息,大幅提高了隧道裂缝识别效率,将识别精度提升至0.2 mm,同时融入优化的Cascade R-CNN算法,在有监督情况下训练隧道裂缝样本,最终实现了隧道裂缝病害的高效提取,同时研发了一套包含硬件数据采集、数据处理软件、数据管理平台的裂缝病害识别路线,真正意义上破除了识别慢、精度低、靠经验、难管理的技术壁垒。

关键词：摄影测量高清工业相机 Cascade R-CNN 隧道裂缝识别深度学习轨道交通

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从统计数据看新冠肺炎疫情对中国经济的影响

经济学动态 2020年第5期2卷 41-51页

作者：许宪春常子豪唐雅清华大学中国经济社会数据研究中心清华大学经济管理学院 100084

2020年一季度,新冠肺炎疫情对中国经济造成严重冲击,三次产业增加值、三大需求、企业利润、一般公共预算收入均下降,居民收入改变了以往平稳增长的走势,增速明显回落。随着疫情得到控制,复工复产加快推进,三四月份经济增长呈现出恢复态... 详细信息

2020年一季度,新冠肺炎疫情对中国经济造成严重冲击,三次产业增加值、三大需求、企业利润、一般公共预算收入均下降,居民收入改变了以往平稳增长的走势,增速明显回落。随着疫情得到控制,复工复产加快推进,三四月份经济增长呈现出恢复态势。本文运用统计数据,从生产、需求、收入、价格角度分析疫情对中国经济的影响。此外,本文也对疫情防控下的新机遇进行了探讨,对中国经济的未来发展进行了展望,期望为在疫情防控状态下继续保持中国经济活力、持续健康发展提供参考。

关键词：新冠疫情中国经济危中有机审慎乐观

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2023年上半年经济形势分析及下半年展望

行政管理改革 2023年第8期8卷 34-47页

作者：许宪春唐雅常子豪北京大学国家发展研究院南京大学政府管理学院中华人民共和国审计署财政审计司

从生产、需求、收入和价格四个角度梳理和分析2023年上半年中国经济运行的若干特点。从生产角度看,上半年经济增速恢复较快,但两年平均增速仍较低;第三产业,特别是线下接触型服务业的快速恢复对经济增速恢复提供了重要支撑。从需求角度... 详细信息

从生产、需求、收入和价格四个角度梳理和分析2023年上半年中国经济运行的若干特点。从生产角度看,上半年经济增速恢复较快,但两年平均增速仍较低;第三产业,特别是线下接触型服务业的快速恢复对经济增速恢复提供了重要支撑。从需求角度看,消费需求增速进一步恢复,对经济增长的拉动作用进一步增强;投资需求对经济增长的贡献率有所减弱;净出口需求进一步走弱,对经济增长的下拉作用加重。从收入角度看,居民收入增速继续回升,规模以上工业企业利润总额持续下降,但降幅逐渐收窄。从价格角度看,消费领域价格涨幅较低,呈下降走势;生产领域价格持续下降,降幅逐月扩大。分析上半年经济增长5.5%背后的含金量,以及经济发展面临的问题与挑战,对2023年下半年经济运行情况进行展望,并提出下半年宏观政策的重点关注方向。

关键词：经济形势经济恢复经济展望

交叉相位引致艾里-厄米-高斯光束在单轴晶体传输时光斑连续转动

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光学学报 2021年第21期41卷 193-199页

作者：朱开成吴梓豪唐慧琴朱洁广州工商学院电子工程系广东广州510850 中南大学物理与电子学院湖南长沙410083 贵州理工学院理学院贵州贵阳550003

按照单轴晶体光束传输理论,求得了具有交叉相位艾里-厄米-高斯光束(AiHGB)在单轴晶体中传输时的解析表达式。数值模拟计算结果表明,具有初始交叉相位的AiHGB在晶体中传输时仍是线偏振的,但不一定是传输不变的。具体地讲,有赖于交叉相位... 详细信息

按照单轴晶体光束传输理论,求得了具有交叉相位艾里-厄米-高斯光束(AiHGB)在单轴晶体中传输时的解析表达式。数值模拟计算结果表明,具有初始交叉相位的AiHGB在晶体中传输时仍是线偏振的,但不一定是传输不变的。具体地讲,有赖于交叉相位系数、光束参数及晶体材料参数,初始交叉相位会引起AiHGB光斑连续转动,在从近场到远场的整个传输过程中总的转动角是90°。并且交叉相位系数足够大时,AiHGB光斑只有纯转动;而交叉相位系数适当小时,AiHGB光斑除了取向转动还有光斑形状变化。这些结果表明通过恰当选择晶体材料(即折射率)和附加交叉相位因子的系数,可以精准调控光束花样形状的取向与结构,也可用于确定晶体材料折射率或测定交叉相位因子的系数。

关键词：物理光学艾里-厄米-高斯光束各向异性单轴晶体交叉相位寻常与非寻常折射指数