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Shear stress distribution prediction in symmetric compound channels using data mining and machine learningmodels

Zohreh SHEIKH KHOZANI, Khabat KHOSRAVI, Mohammadamin TORABI, Amir MOSAVI, Bahram REZAEI, Timon RABCZUK

《结构与土木工程前沿（英文）》 2020年第14卷第5期页码 1097-1109 doi: 10.1007/s11709-020-0634-3

摘要： Shear stress distribution prediction in open channels is of utmost importance in hydraulic structural engineering as it directly affects the design of stable channels. In this study, at first, a series of experimental tests were conducted to assess the shear stress distribution in prismatic compound channels. The shear stress values around the whole wetted perimeter were measured in the compound channel with different floodplain widths also in different flow depths in subcritical and supercritical conditions. A set of, data mining and machine learning algorithms including Random Forest (RF), M5P, Random Committee, KStar and Additive Regression implemented on attained data to predict the shear stress distribution in the compound channel. Results indicated among these five models; RF method indicated the most precise results with the highest value of 0.9. Finally, the most powerful data mining method which studied in this research compared with two well-known analytical models of Shiono and Knight method (SKM) and Shannon method to acquire the proposed model functioning in predicting the shear stress distribution. The results showed that the RF model has the best prediction performance compared to SKM and Shannon models.

关键词： compound channel machine learning SKM model shear stress distribution data mining models

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挖掘变化知识的可拓数据挖掘研究

陈文伟

《中国工程科学》 2006年第8卷第11期页码 70-73

摘要：

规范了可拓信息与可拓知识基本概念，即在信息和知识的概念上扩充了变化的信息和变化的知识。明确了可拓数据挖掘概念以及可拓推理新概念。证明了可拓数据挖掘两个定理和可拓推理公式。提出的从挖掘静态知识的数据挖掘扩展到挖掘变化知识的可拓数据挖掘，为数据挖掘开辟了新的研究方向，并通过实例进行了说明。

关键词：可拓信息可拓知识可拓数据挖掘可拓推理

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Methodological considerations for redesigning sustainable cropping systems: the value of data-mininglarge and detailed farm data sets at the cropping system level

Nicolas MUNIER-JOLAIN, Martin LECHENET

《农业科学与工程前沿（英文）》 2020年第7卷第1期页码 21-27 doi: 10.15302/J-FASE-2019292

摘要：

Redesigning cropping and farming systems to enhance their sustainability is mainly addressed in scientific studies using experimental and modeling approaches. Large data sets collected from real farms allow for the development of innovative methods to produce generic knowledge. Data mining methods allow for the diversity of systems to be considered holistically and can take into account the diversity of production contexts to produce site-specific results. Based on the very few known studies using such methods to analyze the crop management strategies affecting pesticide use and their effect on farm performance, we advocate further investment in the development of large data sets that can support future research programs on farming system design.

关键词： data mining holistic Integrated Pest Management economics DEPHY network.

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A Survey of Tax Risk Detection Using Data Mining Techniques

Qinghua Zheng,Yiming Xu,Huixiang Liu,Bin Shi,Jiaxiang Wang,Bo Dong,

《工程（英文）》 doi: 10.1016/j.eng.2023.07.014

摘要： Tax risk behavior causes serious loss of fiscal revenue, damages the country’s public infrastructure, and disturbs the market economic order of fair competition. In recent years, tax risk detection, driven by information technology such as data mining and artificial intelligence, has received extensive attention. To promote the high-quality development of tax risk detection methods, this paper provides the first comprehensive overview and summary of existing tax risk detection methods worldwide. More specifically, it first discusses the causes and negative impacts of tax risk behavior, along with the development of tax risk detection. It then focuses on data-mining-based tax risk detection methods utilized around the world. Based on the different principles employed by the algorithms, existing risk detection methods can be divided into two categories: relationship-based and non-relationship-based. A total of 14 risk detection methods are identified, and each method is thoroughly explored and analyzed. Finally, four major technical bottlenecks of current data-driven tax risk detection methods are analyzed and discussed, including the difficulty of integrating and using fiscal and tax fragmented knowledge, unexplainable risk detection results, the high cost of risk detection algorithms, and the reliance of existing algorithms on labeled information. After investigating these issues, it is concluded that knowledge-guided and data-driven big data knowledge engineering will be the development trend in the field of tax risk in the future; that is, the gradual transition of tax risk detection from informatization to intelligence is the future development direction.

关键词： Tax risk detection Data mining Knowledge guide Informatization Intellectualization

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Innate immune checkpoint Siglec10 in cancers: mining of comprehensive omics data and validation in patient

《医学前沿（英文）》 2022年第16卷第4期页码 596-609 doi: 10.1007/s11684-021-0868-z

摘要： Sialic acid binding Ig-like lectin 10 (Siglec10) is a member of innate immune checkpoints that inhibits the activation of immune cells through the interaction with its ligand CD24 on tumor cells. Here, by analyzing public databases containing 64 517 patients of 33 cancer types, we found that the expression of Siglec10 was altered in 18 types of cancers and was associated with the clinical outcomes of 11 cancer types. In particular, Siglec10 was upregulated in patients with kidney renal clear cell carcinoma (KIRC) and was inversely associated with the prognosis of the patients. In 131 KIRC patients of our settings, Siglec10 was elevated in the tumor tissues of 83 (63.4%) patients compared with that in their counterpart normal kidney tissues. Moreover, higher level of Siglec10 was associated with advanced disease (stages III and IV) and worse prognosis. Silencing of CD24 in KIRC cells significantly increased the number of Siglec10-expressing macrophages phagocytosing KIRC cells. In addition, luciferase activity assays suggested that Siglec10 was a potential target of the transcription factors c-FOS and GATA1, which were identified by data mining. These results demonstrate that Siglec10 may have important oncogenic functions in KIRC, and represents a novel target for the development of immunotherapies.

关键词： innate immune checkpoint Siglec10 kidney renal clear cell carcinoma

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基于复杂类型数据的发现特征子空间模型(DFSSM)的研究

杨炳儒,唐菁

《中国工程科学》 2003年第5卷第1期页码 56-61

摘要：

探讨围绕知识发现领域中较为宏观、较为重大的问题。首先，根据复杂类型数据（包括Web数据、多媒体数据、空间数据、时间序列数据等）所具有的非线性动力学性质和特征，采用模式（定义为Hilbert空间中的矢量）来定量地表征复杂类型数据的多变性及具有的不确定状态和行为，并用模式的变化来刻画其整体知识发现过程的发展和演变规律；其次，以知识发现系统内在机理的研究为基础，构造了复杂类型数据知识发现系统的总体结构模型——发现特征子空间模型DFSSM；最后，用基于Web的文本挖掘系统和基于图像信息（气象云图）的知识发现系统作为实例进行了验证，结果表明DFSSM方法对于非结构化的文本数据及图像数据类型的知识发现过程具有指导性作用。因此，该结构模型具有较好的实用性与普适性，有望拓展到其他复杂类型数据的知识发现过程中。

关键词：复杂类型数据数据挖掘文本挖掘

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A building-based data capture and data mining technique for air quality assessment

Ni SHENG, U Wa TANG

《环境科学与工程前沿（英文）》 2011年第5卷第4期页码 543-551 doi: 10.1007/s11783-011-0369-4

摘要： Recently, a building-based air quality model system which can predict air quality in front of individual buildings along both sides of a road has been developed. Using the Macau Peninsula as a case study, this paper shows the advantages of building-based model system in data capture and data mining. Compared with the traditional grid-based model systems with input/output spatial resolutions of 1–2 km, the building-based approach can extract the street configuration and traffic data building by building and therefore, can capture the complex spatial variation of traffic emission, urban geometry, and air pollution. The non-homogeneous distribution of air pollution in the Macau Peninsula was modeled in a high-spatial resolution of 319 receptors·km . The spatial relationship among air quality, traffic flow, and urban geometry in the historic urban area is investigated. The study shows that the building-based approach may open an innovative methodology in data mining of urban spatial data for environmental assessment. The results are particularly useful to urban planners when they need to consider the influences of urban form on street environment.

关键词： traffic air pollution spatial distribution high resolution geographic information system

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Data driven models for compressive strength prediction of concrete at high temperatures

Mahmood AKBARI, Vahid JAFARI DELIGANI

《结构与土木工程前沿（英文）》 2020年第14卷第2期页码 311-321 doi: 10.1007/s11709-019-0593-8

摘要： The use of data driven models has been shown to be useful for simulating complex engineering processes, when the only information available consists of the data of the process. In this study, four data-driven models, namely multiple linear regression, artificial neural network, adaptive neural fuzzy inference system, and nearest neighbor models based on collection of 207 laboratory tests, are investigated for compressive strength prediction of concrete at high temperature. In addition for each model, two different sets of input variables are examined: a complete set and a parsimonious set of involved variables. The results obtained are compared with each other and also to the equations of NIST Technical Note standard and demonstrate the suitability of using the data driven models to predict the compressive strength at high temperature. In addition, the results show employing the parsimonious set of input variables is sufficient for the data driven models to make satisfactory results.

关键词： data driven model compressive strength concrete high temperature

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Study of operation optimization based on data mining technique in power plants

LI Jianqiang, LIU Jizhen, GU Junjie, NIU Chenglin

《能源前沿（英文）》 2007年第1卷第4期页码 457-462 doi: 10.1007/s11708-007-0067-1

摘要： The determination of operation optimization value is very important for economic analysis and operation optimization in power plants. The operation optimization value determined by traditional methods usually cannot reflect the ac

关键词： traditional optimization determination economic analysis important

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A review of systematic evaluation and improvement in the big data environment

Feng YANG, Manman WANG

《工程管理前沿（英文）》 2020年第7卷第1期页码 27-46 doi: 10.1007/s42524-020-0092-6

摘要： The era of big data brings unprecedented opportunities and challenges to management research. As one of the important functions of management decision-making, evaluation has been given more functions and application space. Exploring the applicable evaluation methods in the big data environment has become an important subject of research. The purpose of this paper is to provide an overview and discussion of systematic evaluation and improvement in the big data environment. We first review the evaluation methods based on the main analytic techniques of big data such as data mining, statistical methods, optimization and simulation, and deep learning. Focused on the characteristics of big data (association feature, data loss, data noise, and visualization), the relevant evaluation methods are given. Furthermore, we explore the systematic improvement studies and application fields. Finally, we analyze the new application areas of evaluation methods and give the future directions of evaluation method research in a big data environment from six aspects. We hope our research could provide meaningful insights for subsequent research.

关键词： big data evaluation methods systematic improvement big data analytic techniques data mining

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Home location inference from sparse and noisy data: models and applications

Tian-ran HU,Jie-bo LUO,Henry KAUTZ,Adam SADILEK

《信息与电子工程前沿（英文）》 2016年第17卷第5期页码 389-402 doi: 10.1631/FITEE.1500385

摘要： Accurate home location is increasingly important for urban computing. Existing methods either rely on continuous (and expensive) Global Positioning System (GPS) data or suffer from poor accuracy. In particular, the sparse and noisy nature of social media data poses serious challenges in pinpointing where people live at scale. We revisit this research topic and infer home location within 100 m×100 m squares at 70% accuracy for 76% and 71% of active users in New York City and the Bay Area, respectively. To the best of our knowledge, this is the first time home location has been detected at such a fine granularity using sparse and noisy data. Since people spend a large portion of their time at home, our model enables novel applications. As an example, we focus on modeling people’s health at scale by linking their home locations with publicly available statistics, such as education disparity. Results in multiple geographic regions demonstrate both the effectiveness and added value of our home localization method and reveal insights that eluded earlier studies. In addition, we are able to discover the real buzz in the communities where people live.

关键词： Home location Mobility patterns Healthcare

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可拓学在数据挖掘中的应用初探

李立希,李铧汶,杨春燕

《中国工程科学》 2004年第6卷第7期页码 53-59

摘要：

可拓学在数据挖掘中的应用是多方面的，其特点是挖掘“不行变行”的规律。可拓方法丰富了数据挖掘的内容，为多值型关联规则的建立提供了新的工具。提出的可拓数据挖掘模式，有利于利用现存数据更好地为决策服务。

关键词：可拓学可拓集合可拓方法数据挖掘

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关联规则挖掘算法综述

毕建欣,张岐山

《中国工程科学》 2005年第7卷第4期页码 88-94

摘要：

介绍了关联规则挖掘算法的基本原理，并按照挖掘中涉及到的变量数目（维数）、数据的抽象层次和处理变量的类别（布尔型和数值型），依次对关联规则挖掘算法的研究进行综述，并对一些典型的算法进行分析和比较，最后展望了关联规则挖掘算法的研究方向。

关键词：数据挖掘关联规则算法综述

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Data mining diagnosis system based on rough set theory for boilers in thermal power plants

YANG Ping

《机械工程前沿（英文）》 2006年第1卷第2期页码 162-167 doi: 10.1007/s11465-006-0017-z

摘要： Large amounts of data in the SCADA systems databases of thermal power plants have been used for monitoring, control and over-limit alarm, but not for fault diagnosis. Additional tests are often required from the technology support center of manufacturing companies to diagnose faults for large-scale equipment, although these tests are often expensive and involve some risks to equipment. Aimed at difficulties in fault diagnosis for boilers in thermal power plants, a hybrid-intelligence data-mining system based only on acquired data in SCADA systems is structured to extract hidden diagnosis information directly from the SCADA systems databases in thermal power plants. This makes it possible to eliminate additional tests for fault diagnosis. In the system, a focusing quantization algorithm is proposed to discretize all variables in the preparation set to improve resolution near the change between normal value to abnormal value. A reduction algorithm based on rough set theory is designed to find minimum reducts from all discrete variables in the preparation set to represent diagnosis rules succinctly. The diagnosis rules mining from SCADA systems database are expressed directly by variables in the database, making it easy for engineers to understand and use in industry applications. A boiler fault diagnosis system is designed and realized by the proposed approach, its running results in a thermal power plant of Guangdong Province show that the system can satisfy fault diagnosis requirement of large-scale boilers and its accuracy rangers from 91% to 98% in different months.

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基于灰色系统理论的时序数据挖掘技术

刘斌,刘思峰,党耀国

《中国工程科学》 2003年第5卷第9期页码 32-35

摘要：

阐述了嵌入知识的数据挖掘思想和数据挖掘技术现状，结合灰色系统理论首次提出了时序数据挖掘的灰色系统方法集（GDMS），并以灰色系统中的GM（1,1）模型为例，介绍了其具体算法。应用此算法对上海市2002～2005年的上网户数进行了预测。

关键词：灰色系统嵌入知识 GDMS 预测

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标题作者时间类型操作

Shear stress distribution prediction in symmetric compound channels using data mining and machine learningmodels

Zohreh SHEIKH KHOZANI, Khabat KHOSRAVI, Mohammadamin TORABI, Amir MOSAVI, Bahram REZAEI, Timon RABCZUK

期刊论文

挖掘变化知识的可拓数据挖掘研究

陈文伟

期刊论文

Methodological considerations for redesigning sustainable cropping systems: the value of data-mininglarge and detailed farm data sets at the cropping system level

Nicolas MUNIER-JOLAIN, Martin LECHENET

期刊论文

A Survey of Tax Risk Detection Using Data Mining Techniques

Qinghua Zheng,Yiming Xu,Huixiang Liu,Bin Shi,Jiaxiang Wang,Bo Dong,

期刊论文

Innate immune checkpoint Siglec10 in cancers: mining of comprehensive omics data and validation in patient

期刊论文

基于复杂类型数据的发现特征子空间模型(DFSSM)的研究

杨炳儒,唐菁

期刊论文

A building-based data capture and data mining technique for air quality assessment

Ni SHENG, U Wa TANG