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Assessment of temporal and spatial variations in water quality using multivariate statistical methods

Xue LI,Pengjing LI,Dong WANG,Yuqiu WANG

《环境科学与工程前沿（英文）》 2014年第8卷第6期页码 895-904 doi: 10.1007/s11783-014-0736-z

摘要： This study evaluated the temporal and spatial variations of water quality data sets for the Xin'anjiang River through the use of multivariate statistical techniques, including cluster analysis (CA), discriminant analysis (DA), correlation analysis, and principal component analysis (PCA). The water samples, measured by ten parameters, were collected every month for three years (2008–2010) from eight sampling stations located along the river. The hierarchical CA classified the 12 months into three periods (First, Second and Third Period) and the eight sampling sites into three groups (Groups 1, 2 and 3) based on seasonal differences and various pollution levels caused by physicochemical properties and anthropogenic activities. DA identified three significant parameters (temperature, pH and ) to distinguish temporal groups with close to 76% correct assignment. The DA also discovered five parameters (temperature, electricity conductivity, total nitrogen, chemical oxygen demand and total phosphorus) for spatial variation analysis, with 80.56% correct assignment. The non–parametric correlation coefficient (Spearman R) explained the relationship between the water quality parameters and the basin characteristics, and the GIS made the results visual and direct. The PCA identified four PCs for Groups 1 and 2, and three PCs for Group 3. These PCs captured 68.94%, 67.48% and 70.35% of the total variance of Groups 1, 2 and 3, respectively. Although natural pollution affects the Xin'anjiang River, the main sources of pollution included agricultural activities, industrial waste, and domestic wastewater.

关键词： Xin'anjiang River multivariable statistical analysis temporal variation spatial variation water quality

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Modeling of unconfined compressive strength of soil-RAP blend stabilized with Portland cement using multivariate

Ali Reza GHANIZADEH, Morteza RAHROVAN

《结构与土木工程前沿（英文）》 2019年第13卷第4期页码 787-799 doi: 10.1007/s11709-019-0516-8

摘要： The recycled layer in full-depth reclamation (FDR) method is a mixture of coarse aggregates and reclaimed asphalt pavement (RAP) which is stabilized by a stabilizer agent. For design and quality control of the final product in FDR method, the unconfined compressive strength of stabilized material should be known. This paper aims to develop a mathematical model for predicting the unconfined compressive strength (UCS) of soil-RAP blend stabilized with Portland cement based on multivariate adaptive regression spline (MARS). To this end, two different aggregate materials were mixed with different percentages of RAP and then stabilized by different percentages of Portland cement. For training and testing of MARS model, total of 64 experimental UCS data were employed. Predictors or independent variables in the developed model are percentage of RAP, percentage of cement, optimum moisture content, percent passing of #200 sieve, and curing time. The results demonstrate that MARS has a great ability for prediction of the UCS in case of soil-RAP blend stabilized with Portland cement ( is more than 0.97). Sensitivity analysis of the proposed model showed that the cement, optimum moisture content, and percent passing of #200 sieve are the most influential parameters on the UCS of FDR layer.

关键词： full-depth reclamation soil-reclaimed asphalt pavement blend Portland cement unconfined compressive strength multivariate adaptive regression spline

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三峡库区香溪河流域多变量生态水文风险的不确定性分析 Article

Yurui Fan,Guohe Huang,Yin Zhang,Yongping Li

《工程（英文）》 2018年第4卷第5期页码 617-626 doi: 10.1016/j.eng.2018.06.006

摘要：

本研究基于copula函数开发了一种多变量生态水文风险评估框架，用于分析三峡库区香溪河流域极端生态水文事件的发生频率。通过马尔可夫链蒙特卡罗（MCMC）方法量化边缘分布及copula函数中参数的不确定性，并基于后验概率揭示联合重现期的内在不确定性，同时可进一步得到双变量及多变量风险的概率特征。研究结果显示所得概率模型的预测区间可很好地匹配观测值，尤其对洪水持续时间而言。同时，“AND”联合重现期的不确定性随着单个洪水变量重现期的增加而增加。此外，低设计流量及高服务年限可能导致高洪水风险且伴随大量不确定性。

关键词：洪水风险 copula 多变量水文频率分析概率分布马尔科夫链蒙特卡罗

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Performance evaluation of circulating fluidized bed incineration of municipal solid waste by multivariate

Hua Tao, Pinjing He, Yi Zhang, Wenjie Sun

《环境科学与工程前沿（英文）》 2017年第11卷第6期 doi: 10.1007/s11783-017-0945-3

摘要： This first nationwide survey was conducted to evaluate the overall performance of the circulating fluidized bed (CFB) incineration of municipal solid waste (MSW) in 2014-2015 in China. Total 23 CFB incineration power plants were evaluated. The data for monthly average flue gas emission of particles, CO, NO , SO and HCl were collected over 12 consecutive months. The data were analyzed to assess the overall performance of CFB incineration by applying the Mahalanobis distance as a multivariate outlier detection method. Although the flue gas emission parameters had met the Chinese national emission standards, there were 11 total outliers (abnormal behavior) detected in 6 out of 23 CFB incineration power plants from the perspective of the MSW incineration performance. The results demonstrate that it is more important for a better performance of CFBs to reduce the frequencies of the MSW load changes, rather than the magnitudes of the MSW load changes, particularly reducing the frequencies in the range of 10% and more of the load changes, under the same and stable conditions. Furthermore, the overloading occurs more often than the underloading during the operation of the CFB incineration power plants in China. The frequent overloading is 0% to 30% of the designed capacity. To achieve the stable performance of CFBs in practice, an appropriately designed MSW storage capacity is suggested to build in a plant to buffer and reduce the frequency of the load changes.

关键词： Municipal solid waste Incineration Circulating fluidized bed Load change Multivariate outlier detection

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Image-based fall detection and classification of a user with a walking support system

Sajjad TAGHVAEI, Kazuhiro KOSUGE

《机械工程前沿（英文）》 2018年第13卷第3期页码 427-441 doi: 10.1007/s11465-017-0465-7

摘要：

The classification of visual human action is important in the development of systems that interact with humans. This study investigates an image-based classification of the human state while using a walking support system to improve the safety and dependability of these systems. We categorize the possible human behavior while utilizing a walker robot into eight states (i.e., sitting, standing, walking, and five falling types), and propose two different methods, namely, normal distribution and hidden Markov models (HMMs), to detect and recognize these states. The visual feature for the state classification is the centroid position of the upper body, which is extracted from the user’s depth images. The first method shows that the centroid position follows a normal distribution while walking, which can be adopted to detect any non-walking state. The second method implements HMMs to detect and recognize these states. We then measure and compare the performance of both methods. The classification results are employed to control the motion of a passive-type walker (called “RT Walker”) by activating its brakes in non-walking states. Thus, the system can be used for sit/stand support and fall prevention. The experiments are performed with four subjects, including an experienced physiotherapist. Results show that the algorithm can be adapted to the new user’s motion pattern within 40 s, with a fall detection rate of 96.25% and state classification rate of 81.0%. The proposed method can be implemented to other abnormality detection/classification applications that employ depth image-sensing devices.

关键词： fall detection walking support hidden Markov model multivariate analysis

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Spatio-temporal variations of water quality in Yuqiao Reservoir Basin, North China

Yuan XU,Ruqin XIE,Yuqiu WANG,Jian SHA

《环境科学与工程前沿（英文）》 2015年第9卷第4期页码 649-664 doi: 10.1007/s11783-014-0702-9

摘要： Fuzzy comprehensive assessment and multivariate statistical techniques including cluster analysis, discriminant analysis, principal component analysis, and factor analysis were applied to analyze the water quality status of Yuqiao Reservoir Basin, North China, for assessing its spatio-temporal variations and identifying potential pollution sources. In this paper, we considered data for 14 water quality parameters collected during 1990–2004 at 7 water quality monitoring sites. The results of fuzzy comprehensive assessment revealed that water quality in Yuqiao Reservoir Basin showed a downtrend from 1990 to 2001 with fluctuation, and a slowly upward trend after 2001. The major water quality belonged to Class III and IV. Besides, hierarchical cluster analysis divided 7 monitoring sites into two groups (Group A and B), and 12 months into three periods (low-flow (LF), normal-flow (NF), and high-flow (HF) period). Temp, pH, SS, T-har, DO, NO -N and TP were identified as significant variables affecting spatial variations, and Temp, pH and NO -N were identified as significant variables affecting temporal variations by discriminant analysis. Factor analysis identified four latent pollution sources for water quality variations: nutrient pollution, organic pollution, inorganic pollution, and natural pollution. Moreover, for Group A regions, pollution inputs mainly came from domestic wastewater and industrial sewage. For Group B regions, it is more likely that water pollution resulted from the combined effects of domestic wastewater, hospital wastewater, agriculture runoff, and fishpond discharge, as well as the incoming water from upstream.

关键词： Fuzzy comprehensive assessment multivariate statistical analysis water quality

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Field investigation of intelligent compaction for hot mix asphalt resurfacing

Wei HU,Xiang SHU,Baoshan HUANG,Mark WOODS

《结构与土木工程前沿（英文）》 2017年第11卷第1期页码 47-55 doi: 10.1007/s11709-016-0362-x

摘要：

Intelligent compaction (IC) is a relatively new technology for asphalt paving industry. The present study evaluated the effectiveness and potential issues of the IC technology for flexible pavement resurfacing construction using two field projects. In the first project, a geostatistical semivariogram model was established and the parameters derived from it were compared with univariate statistical parameters for the Compaction Meter Value (CMV) data. Further analyses illustrated the effect of temperature on the CMV value and compaction uniformity. In the second project, a multivariate analysis was performed between in situ tests and IC data. The possibility of combining various IC data to predict the asphalt layer density and improve the current quality control and assurance system was discussed.

关键词： intelligent compaction compaction meter value (CMV) semivariogram multivariate analysis

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基于多元数据的交通视角超大特大城市中心城区合理规模研究

陆化普,柏卓彤,吴洲豪,傅志寰

《中国工程科学》 2022年第24卷第6期页码 146-153 doi: 10.15302/J-SSCAE-2022.06.013

摘要：

超大特大城市中心城区高强度连片开发、人口密度大、城市功能集中，是我国城市问题表现最为突出的区域范围；着眼集中于中心城区的大城市病破解问题，开展超大特大城市中心城区的合理规模分析论证具有迫切性。本文提出了通勤出行时间是超大特大城市中心城区合理规模的核心控制因素这一基本判断；采用大数据分析及聚类分析方法，结合城市多类土地利用的兴趣点数据、街道行政边界的地理信息系统数据，识别了我国10 个超大特大城市的现状中心城区范围；基于网络地图路径规划、手机信令数据校核，分析评价了现状交通效率；以量化分析为基础，获得了特大城市中心城区合理规模的论证结果。研究表明，当前一些超大特大城市的中心城区范围不能满足以人为本的幸福通勤出行需求；结合未来交通运输领域技术发展、治理水平提高等因素，13~15 km当量半径是超大特大城市中心城区合理规模范围的上限。

关键词：合理规模；多元数据；中心城区；超大特大城市；幸福感

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一种直观的一般秩相关系数 Research Articles

Divya PANDOVE, Shivani GOEL, Rinkle RANI

《信息与电子工程前沿（英文）》 2018年第19卷第6期页码 699-711 doi: 10.1631/FITEE.1601549

摘要：相关分析是研究数据模式和预测的有效机制。在看似无关的数据中建立相关性可得到许多有趣发现。提出一种算法，用于量化相关性理论并得出一个直观且更精确的相关系数。为计算配对值之间相关性，提出一项预测指标，称为一般秩相关系数。其满足预测指标的5个基本标准：样本规模的独立性、数值介于−1与1之间、测量单调性程度、对异常值不敏感性、直观演示。此外，使用实时数据集和随机数模拟实验对该指标进行验证。同时，展示了所提方程的数学推导过程，并与斯皮尔曼等级相关系数比较。结果表明，该指标在所有预测度量标准上均优于现存指标。

关键词：一般秩相关系数；多变量分析；预测指标；斯皮尔曼等级相关系数

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卡特里娜飓风的启示——有关海洋和水利工程的风险分析

刘德辅,庞亮,谢波涛,史宏达,逯义军

《中国工程科学》 2007年第9卷第10期页码 24-29

摘要：

2005年卡特里娜（Katrina）和丽塔（Rita）飓风对美国新奥尔良市和佛罗里达东部海岸带来的灾难性破坏，验证了笔者在20世纪80年代初期提出的复合极值分布理论及其对上述海域飓风强度预测结果的正确性。以此为鉴，讨论了海岸、近海、水利和城市防灾工程中引入不确定性分析和多维联合概率理论进行风险分析的必要性。

关键词：卡特里娜飓风复合极值分布多维复合极值分布不确定性分析海洋风险分析

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扩大多元回归方法在跨组学研究中的范围 Article

Xiaoxi Hu, Yue Ma, Yakun Xu, Peiyao Zhao, Jun Wang

《工程（英文）》 2021年第7卷第12期页码 1725-1731 doi: 10.1016/j.eng.2020.05.028

摘要：

近年来科技的进步和发展使得高维数据急剧增加，研究人员对合适且有效的多元回归方法的需求也随之增长。许多传统的多元分析方法如主成分分析等已广泛应用于投资分析、图像识别和群体遗传结构分析等研究领域。然而，这些常见的方法存在其局限性，即忽略了响应之间的相关性和变量选择效率低的问题。因此，本文引入了降秩回归方法及其扩展形式——稀疏降秩回归和行稀疏的子空间辅助回归，这些方法有望满足上述需求，从而提高回归模型的可解释性。我们通过开展仿真研究来评估它们的效果，并将它们与其他几种变量选择方法进行比较。对于不同的应用场景，我们也提供了基于预测能力和变量选择精度的选择建议。最后，为了证明这些方法在微生物组研究领域的实用价值，我们将所选择的方法应用于实际种群水平的微生物组数据，结果验证了我们方法的有效性。该方法的扩展形式为未来的组学研究特别是多元回归研究提供了有价值的指导，并为微生物组学及其相关研究领域的新发现奠定了基础。

关键词：多元回归方法降秩回归稀疏性降维变量选择

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Unknown fault detection for EGT multi-temperature signals based on self-supervised feature learning and unary classification

《能源前沿（英文）》 2023年第17卷第4期页码 527-544 doi: 10.1007/s11708-023-0880-x

摘要： Intelligent power systems can improve operational efficiency by installing a large number of sensors. Data-based methods of supervised learning have gained popularity because of available Big Data and computing resources. However, the common paradigm of the loss function in supervised learning requires large amounts of labeled data and cannot process unlabeled data. The scarcity of fault data and a large amount of normal data in practical use pose great challenges to fault detection algorithms. Moreover, sensor data faults in power systems are dynamically changing and pose another challenge. Therefore, a fault detection method based on self-supervised feature learning was proposed to address the above two challenges. First, self-supervised learning was employed to extract features under various working conditions only using large amounts of normal data. The self-supervised representation learning uses a sequence-based Triplet Loss. The extracted features of large amounts of normal data are then fed into a unary classifier. The proposed method is validated on exhaust gas temperatures (EGTs) of a real-world 9F gas turbine with sudden, progressive, and hybrid faults. A comprehensive comparison study was also conducted with various feature extractors and unary classifiers. The results show that the proposed method can achieve a relatively high recall for all kinds of typical faults. The model can detect progressive faults very quickly and achieve improved results for comparison without feature extractors in terms of F1 score.

关键词： fault detection unary classification self-supervised representation learning multivariate nonlinear time series

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基于回归预测集成学习的交互式图像分割 Article

Jin ZHANG, Zhao-hui TANG, Wei-hua GUI, Qing CHEN, Jin-ping LIU

《信息与电子工程前沿（英文）》 2017年第18卷第7期页码 1002-1020 doi: 10.1631/FITEE.1601401

摘要：首先，基于已标记样本训练出两个在属性上互补的多元自适应回归样条学习器（multivariate adaptive regression splines, MARS）和薄板样条回归学习器（thin plate

关键词：交互式图像分割；多元自适应回归样条；集成学习；薄板样条回归；半监督学习；支持向量回归

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Decomposition analysis applied to energy and emissions: A literature review

《工程管理前沿（英文）》页码 625-639 doi: 10.1007/s42524-023-0270-4

摘要： Decomposition analysis has been widely used to assess the determinants of energy and CO₂ emissions in academic research and policy studies. Both the methodology and application of decomposition analysis have been largely improved in the past decades. After more than 50 years’ developments, decomposition studies have become increasingly sophisticated and diversified, and tend to converge internally and integrate with other analytical approaches externally. A good understanding of the literature and state of the art is critical to identify knowledge gaps and formulate future research agenda. To this end, this study presents a literature survey for decomposition analysis applied to energy and emission issues, with a focus on the period of 2016–2021. A review for three individual decomposition techniques is first conducted, followed by a synthesis of emerging trends and features for the decomposition analysis literature as a whole. The findings are expected to direct future research in decomposition analysis.

关键词： index decomposition analysis structural decomposition analysis production decomposition analysis energy CO₂ emissions

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Flow, thermal, and vibration analysis using three dimensional finite element analysis for a flux reversal

B. VIDHYA,K. N. SRINIVAS

《能源前沿（英文）》 2016年第10卷第4期页码 424-440 doi: 10.1007/s11708-016-0423-9

摘要： This paper presents the simulation of major mechanical properties of a flux reversal generator (FRG) viz., computational fluid dynamic (CFD), thermal, and vibration. A three-dimensional finite element analysis (FEA) based CFD technique for finding the spread of pressure and air velocity in air regions of the FRG is described. The results of CFD are mainly obtained to fine tune the thermal analysis. Thus, in this focus, a flow analysis assisted thermal analysis is presented to predict the steady state temperature distribution inside FRG. The heat transfer coefficient of all the heat producing inner walls of the machine are evaluated from CFD analysis, which forms the main factor for the prediction of accurate heat distribution. The vibration analysis is illustrated. Major vibration sources such as mechanical, magnetic and applied loads are covered elaborately which consists of a 3D modal analysis to find the natural frequency of FRG, a 3D static stress analysis to predict the deformation of the stator, rotor and shaft for different speeds, and an unbalanced rotor harmonic analysis to find eccentricity of rotor to make sure that the vibration of the rotor is within the acceptable limits. Harmonic analysis such as sine sweep analysis to identify the range of speeds causing high vibrations and steady state vibration at a mode frequency of 1500 Hz is presented. The vibration analysis investigates the vibration of the FRG as a whole, which forms the contribution of this paper in the FRG literature.

关键词： flux reversal generator air velocity computation fluid dynamics thermal analysis vibration analysis finite element analysis