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摘要 ：
大气污染领域本体的半自动构建及语义推理
关键词 ：
语义推理 语义推理 注意力机制 注意力机制 大气污染 大气污染 自然语言处理 自然语言处理 实体关系抽取 实体关系抽取 本体 本体
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| GB/T 7714 | 刘博 , 张佳慧 , 李建强 et al. 大气污染领域本体的半自动构建及语义推理 [J]. | 刘博 , 2021 , 47 (3) : 246-259 . |
| MLA | 刘博 et al. "大气污染领域本体的半自动构建及语义推理" . | 刘博 47 . 3 (2021) : 246-259 . |
| APA | 刘博 , 张佳慧 , 李建强 , 李永 , 郎建垒 , 北京工业大学学报 . 大气污染领域本体的半自动构建及语义推理 . | 刘博 , 2021 , 47 (3) , 246-259 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
一种生成式问答的评价方法涉及自然语言处理领域。本发明针对生成式问答的答案在语言质量和语义准确度上存在的各种问题，分别构建数据集的正负样本。通过在数据集构建上进行处理，就能使问答评价网络在训练后能够结合语言质量和语义准确性两方面对生成的答案进行打分。相比传统的评价方法对答案的评价更全面、准确。
引用：
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| GB/T 7714 | 刘博 , 王星文 , 徐宽 et al. 一种生成式问答的评价方法 : CN202110184397.4[P]. | 2021-02-10 . |
| MLA | 刘博 et al. "一种生成式问答的评价方法" : CN202110184397.4. | 2021-02-10 . |
| APA | 刘博 , 王星文 , 徐宽 , 胡志超 . 一种生成式问答的评价方法 : CN202110184397.4. | 2021-02-10 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
本实验对3组同规格SBR反应器分别采用分阶段法(A/O-A/O/A)异步驯化、连续曝气A/OA同步驯化和间歇曝气A/O/A同步驯化的方式运行.以人工配水为进水基质,接种絮状污泥,通过水力选择压颗粒化,探讨了不同运行方式下短程硝化反硝化颗粒污泥的驯化及脱氮除磷特性.结果表明,在较短曝气时长(140 min)联合较低曝气强度[3.5 L·(h·L)~(-1)]下,间歇曝气A/O/A同步驯化最具优势,后期稳定运行期间碳、氮、磷的平均去除率分别为90.74%、91.15%和95.66%,可实现同步去除.粒径为895μm,颗粒虽小但均匀致密,f值(MLVSS/MLSS)平稳保持在0.8～0.85,有较高...
关键词 ：
同步驯化 同步驯化 曝气强度 曝气强度 曝气时长 曝气时长 短程硝化反硝化除磷 短程硝化反硝化除磷 间歇曝气 间歇曝气 颗粒污泥 颗粒污泥
引用：
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| GB/T 7714 | 王文琪 , 李冬 , 高鑫 et al. 短程硝化反硝化除磷颗粒污泥的同步驯化 [J]. | 环境科学 , 2021 , 42 (06) : 2946-2956 . |
| MLA | 王文琪 et al. "短程硝化反硝化除磷颗粒污泥的同步驯化" . | 环境科学 42 . 06 (2021) : 2946-2956 . |
| APA | 王文琪 , 李冬 , 高鑫 , 刘博 , 张杰 . 短程硝化反硝化除磷颗粒污泥的同步驯化 . | 环境科学 , 2021 , 42 (06) , 2946-2956 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
为了明确大气污染物、污染源、影响因素、评价指标、危害等之间的关系,分析大气污染传播路径,建立了一个较为清晰、完善的大气污染领域本体.首先,基于机器学习和自然语言处理等技术,提出一种基于注意力机制的序列标注联合抽取实体关系的方法,在双向长短时记忆(long short-term memory,LSTM)网络模型中加入注意力机制,并将实体和关系联合标注,从而进行实体关系抽取.其次,结合词频-逆文档频率(term frequency-inverse document frequency,TF-IDF)核心概念挖掘方法进行知识抽取,并将概念、属性、关系和实例组织起来,从而实现大气污染本体模型的半自动构建.最后,在本体和实例的基础上通过Protégé的SPARQL Query模块和HermiT推理机分别进行条件推理和可视化推理.结果表明,基于注意力机制的序列标注实体关系联合抽取方法所构建的大气污染领域本体包含核心实体68个,实例数360个,相较于现有的本领域本体,在全面性、有效性、准确性和可重用性方面都有较好表现,同时推理出了Ca2+和K+等污染离子的传播路径.因此,基于注意力机制的序列标注联合抽取实体关系的方法能够有效地半自动构建大气污染领域本体,推理出清晰的大气污染传播路径.
关键词 ：
大气污染 大气污染 实体关系抽取 实体关系抽取 本体 本体 注意力机制 注意力机制 自然语言处理 自然语言处理 语义推理 语义推理
引用：
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| GB/T 7714 | 刘博 , 张佳慧 , 李建强 et al. 大气污染领域本体的半自动构建及语义推理 [J]. | 北京工业大学学报 , 2021 , 47 (3) : 246-259 . |
| MLA | 刘博 et al. "大气污染领域本体的半自动构建及语义推理" . | 北京工业大学学报 47 . 3 (2021) : 246-259 . |
| APA | 刘博 , 张佳慧 , 李建强 , 李永 , 郎建垒 . 大气污染领域本体的半自动构建及语义推理 . | 北京工业大学学报 , 2021 , 47 (3) , 246-259 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
对深度学习模型应用于时空序列预测的最新进展进行总结.首先介绍时空序列数据的属性及类型,并进行相应的实例化与表示.接着针对时空序列数据存在的3个问题分别提出相应的数据预处理方法,对基于传统参数模型、传统机器学习模型以及深度学习模型的时空序列预测方法逐一阐述并对比分析,为研究者选择模型提供指导,之后总结深度学习模型在不同领域内对时空序列预测的应用.最后指出当前研究的不足以及时空序列预测进一步的研究方向.
引用：
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| GB/T 7714 | 刘博 , 王明烁 , 李永 et al. 深度学习在时空序列预测中的应用综述 [J]. | 北京工业大学学报 , 2021 , 47 (8) : 925-941 . |
| MLA | 刘博 et al. "深度学习在时空序列预测中的应用综述" . | 北京工业大学学报 47 . 8 (2021) : 925-941 . |
| APA | 刘博 , 王明烁 , 李永 , 陈洪丽 , 李建强 . 深度学习在时空序列预测中的应用综述 . | 北京工业大学学报 , 2021 , 47 (8) , 925-941 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
The implementation of two-dimensional materials into memristor architectures has recently been a new research focus by taking advantage of their atomic thickness, unique lattice, and physical and electronic properties. Among the van der Waals family, Bi2O2Se is an emerging ternary two-dimensional layered material with ambient stability, suitable band structure, and high conductivity that exhibits high potential for use in electronic applications. In this work, we propose and experimentally demonstrate a Bi2O2Se-based memristor-aided logic. By carefully tuning the electric field polarity of Bi2O2Se through a Pd contact, a reconfigurable NAND gate with zero static power consumption is realized. To provide more knowledge on NAND operation, a kinetic Monte Carlo simulation is carried out. Because the NAND gate is a universal logic gate, cascading additional NAND gates can exhibit versatile logic functions. Therefore, the proposed Bi2O2Se-based MAGIC can be a promising building block for developing next-generation in-memory logic computers with multiple functions.
关键词 ：
Bi2O2Se Bi2O2Se CAFM CAFM kinetic Monte Carlo kinetic Monte Carlo MAGIC MAGIC RRAM RRAM
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| GB/T 7714 | Liu Bo , Zhao Yudi , Verma Dharmendra et al. Bi2O2Se-Based Memristor-Aided Logic. [J]. | ACS applied materials & interfaces , 2021 , 13 (13) : 15391-15398 . |
| MLA | Liu Bo et al. "Bi2O2Se-Based Memristor-Aided Logic." . | ACS applied materials & interfaces 13 . 13 (2021) : 15391-15398 . |
| APA | Liu Bo , Zhao Yudi , Verma Dharmendra , Wang Le An , Liang Hanyuan , Zhu Hui et al. Bi2O2Se-Based Memristor-Aided Logic. . | ACS applied materials & interfaces , 2021 , 13 (13) , 15391-15398 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
As an important method to diagnose gastric cancer, gastric pathological sections images (GPSI) are hard and time-consuming to be recognized even by an experienced doctor. An efficient method was designed to detect gastric cancer in magnified (20x) GPSI using deep learning technology. A novel DenseNet architecture was applied, modified with a multistage attention module (MSA-DenseNet). To develop this model focusing on gastric features, a two-stage-input attention module was adopted to select more semantic information of cancer. Moreover, the pretraining process was divided into two steps to improve the effect of the attention mechanism. After training, our method achieved a state-of-the-art performance yielding 0.9947 F1 score and 0.9976 ROC AUC on a test dataset. In line with our expectation in clinical practice, a high recall (0.9929) was produced with high sensitivity to the positive samples. These results indicate that this new model performs better than current artificial detection approaches and its effectiveness is therefore validated in cancer pathological diagnoses.
关键词 ：
computer‐ computer‐ assisted diagnosis assisted diagnosis gastric pathological sections gastric pathological sections gastric cancer gastric cancer deep learning deep learning
引用：
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| GB/T 7714 | Liu, Bo , Zhao, Yelong , Yang, Bin et al. A gastric cancer recognition algorithm on gastric pathological sections based on multistage attention-DenseNet [J]. | CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE , 2021 , 33 (10) . |
| MLA | Liu, Bo et al. "A gastric cancer recognition algorithm on gastric pathological sections based on multistage attention-DenseNet" . | CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 33 . 10 (2021) . |
| APA | Liu, Bo , Zhao, Yelong , Yang, Bin , Zhao, Shuangtao , Gu, Rentao , Gahegan, Mark . A gastric cancer recognition algorithm on gastric pathological sections based on multistage attention-DenseNet . | CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE , 2021 , 33 (10) . |
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摘要 ：
Atmospheric visibility is an indicator of atmospheric transparency and its range directly reflects the quality of the atmospheric environment. With the acceleration of industrialization and urbanization, the natural environment has suffered some damages. In recent decades, the level of atmospheric visibility shows an overall downward trend. A decrease in atmospheric visibility will lead to a higher frequency of haze, which will seriously affect people's normal life, and also have a significant negative economic impact. The causal relationship mining of atmospheric visibility can reveal the potential relation between visibility and other influencing factors, which is very important in environmental management, air pollution control and haze control. However, causality mining based on statistical methods and traditional machine learning techniques usually achieve qualitative results that are hard to measure the degree of causality accurately. This article proposed the seq2seq-LSTM Granger causality analysis method for mining the causality relationship between atmospheric visibility and its influencing factors. In the experimental part, by comparing with methods such as linear regression, random forest, gradient boosting decision tree, light gradient boosting machine, and extreme gradient boosting, it turns out that the visibility prediction accuracy based on the seq2seq-LSTM model is about 10% higher than traditional machine learning methods. Therefore, the causal relationship mining based on this method can deeply reveal the implicit relationship between them and provide theoretical support for air pollution control.
关键词 ：
granger causality granger causality deep learning deep learning multidimensional time series multidimensional time series Atmospheric visibility Atmospheric visibility
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| GB/T 7714 | Liu, Bo , He, Xi , Song, Mingdong et al. A Method for Mining Granger Causality Relationship on Atmospheric Visibility [J]. | ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA , 2021 , 15 (5) . |
| MLA | Liu, Bo et al. "A Method for Mining Granger Causality Relationship on Atmospheric Visibility" . | ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA 15 . 5 (2021) . |
| APA | Liu, Bo , He, Xi , Song, Mingdong , Li, Jiangqiang , Qu, Guangzhi , Lang, Jianlei et al. A Method for Mining Granger Causality Relationship on Atmospheric Visibility . | ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA , 2021 , 15 (5) . |
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摘要 ：
In this paper, we consider the optimal approximations of univariate functions with feed-forward ReLU neural networks. We attempt to answer the following questions. For given function and network, what is the minimal possible approximation error? How fast does the optimal approximation error decrease with network size? Is optimal approximation attainable by current network training techniques? Theoretically, we introduce necessary and sufficient conditions for optimal approximations of convex functions. We give lower and upper bounds of optimal approximation errors, and approximation rate that measures how fast approximation error decreases with network size. ReLU network architectures are presented to generate optimal approximations. We then propose an algorithm to compute optimal approximations and prove its convergence. We conduct experiments to validate its effectiveness and compare with other approaches. We also demonstrate that the theoretical limit of approximation errors is not attained by ReLU networks trained with stochastic gradient descent optimization, which indicates that the expressive power of ReLU networks has not been exploited to its full potential. (c) 2021 Elsevier B.V. All rights reserved.
关键词 ：
ReLU networks ReLU networks Expressive power Expressive power Deep learning theory Deep learning theory Optimal approximation Optimal approximation
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| GB/T 7714 | Liu, Bo , Liang, Yi . Optimal function approximation with ReLU neural networks [J]. | NEUROCOMPUTING , 2021 , 435 : 216-227 . |
| MLA | Liu, Bo et al. "Optimal function approximation with ReLU neural networks" . | NEUROCOMPUTING 435 (2021) : 216-227 . |
| APA | Liu, Bo , Liang, Yi . Optimal function approximation with ReLU neural networks . | NEUROCOMPUTING , 2021 , 435 , 216-227 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
With rapid industrial development, air pollution problems, especially in urban and metropolitan centers, have become a serious societal problem and require our immediate attention and comprehensive solutions to protect human and animal health and the environment. Because bad air quality brings prominent effects on our daily life, how to forecast future air quality accurately and tenuously has emerged as a priority for guaranteeing the quality of human life in many urban areas worldwide. Existing models usually neglect the influence of wind and do not consider both distance and similarity to select the most related stations, which can provide significant information in prediction. Therefore, we propose a Geographic Self-Organizing Map (GeoSOM) spatiotemporal gated recurrent unit (GRU) model, which clusters all the monitor stations into several clusters by geographical coordinates and time-series features. For each cluster, we build a GRU model and weighted different models with the Gaussian vector weights to predict the target sequence. The experimental results on real air quality data in Beijing validate the superiority of the proposed method over a number of state-of-the-art ones in metrics, such as R-2, mean relative error (MRE), and mean absolute error (MAE). The MAE, MRE, and R-2 are 16.1, 0.79, and 035 at the Gucheng station and 19.53, 0.82, and 036 at the Dongsi station.
关键词 ：
Air quality Air quality environment pollution environment pollution prediction prediction recurrent neural network (RNN) recurrent neural network (RNN) spatiotemporal sequences spatiotemporal sequences
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| GB/T 7714 | Liu, Bo , Yan, Shuo , Li, Jianqiang et al. A Spatiotemporal Recurrent Neural Network for Prediction of Atmospheric PM2.5: A Case Study of Beijing [J]. | IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS , 2021 , 8 (3) : 578-588 . |
| MLA | Liu, Bo et al. "A Spatiotemporal Recurrent Neural Network for Prediction of Atmospheric PM2.5: A Case Study of Beijing" . | IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 8 . 3 (2021) : 578-588 . |
| APA | Liu, Bo , Yan, Shuo , Li, Jianqiang , Li, Yong , Lang, Jianlei , Qu, Guangzhi . A Spatiotemporal Recurrent Neural Network for Prediction of Atmospheric PM2.5: A Case Study of Beijing . | IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS , 2021 , 8 (3) , 578-588 . |
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