资源类型

期刊论文 1543

年份

2024 1

2023 59

2022 101

2021 100

2020 74

2019 105

2018 90

2017 86

2016 67

2015 85

2014 84

2013 68

2012 79

2011 71

2010 73

2009 49

2008 75

2007 87

2006 36

2005 27

展开 ︾

关键词

风险分析 9

分析 4

可持续发展 4

对策 4

影响因素 4

数值模拟 4

隧道 4

ANSYS 3

数值分析 3

裂缝 3

2035年 2

BNLAS 2

COVID-19 2

DX桩 2

HIV感染孕产妇 2

“一带一路” 2

专利分析 2

仿真 2

制造业 2

展开 ︾

检索范围：

排序： 展示方式：

Multi-objective optimization of cutting parameters in high-speed milling based on grey relational analysiscoupled with principal component analysis

Tao FU, Jibin ZHAO, Weijun LIU

《机械工程前沿（英文）》 2012年 第7卷 第4期   页码 445-452 doi: 10.1007/s11465-012-0338-z

摘要：

This paper investigates optimization problem of the cutting parameters in high-speed milling on NAK80 mold steel. An experiment based on the technology of Taguchi is performed. The objective is to establish a correlation among spindle speed, feed per tooth and depth of cut to the three directions of cutting force in the milling process. In this study, the optimum cutting parameters are obtained by the grey relational analysis. Moreover, the principal component analysis is applied to evaluate the weights so that their relative significance can be described properly and objectively. The results of experiments show that grey relational analysis coupled with principal component analysis can effectively acquire the optimal combination of cutting parameters and the proposed approach can be a useful tool to reduce the cutting force.

关键词： high-speed milling     grey relational analysis     principal component analysis     parameters optimization    

HTML PDF 收藏

Non-negativematrix factorization based unmixing for principal component transformed hyperspectral data

Xiu-rui GENG,Lu-yan JI,Kang SUN

《信息与电子工程前沿（英文）》 2016年 第17卷 第5期   页码 403-412 doi: 10.1631/FITEE.1600028

摘要： Non-negative matrix factorization (NMF) has been widely used in mixture analysis for hyperspectral remote sensing. When used for spectral unmixing analysis, however, it has two main shortcomings: (1) since the dimensionality of hyperspectral data is usually very large, NMF tends to suffer from large computational complexity for the popular multiplicative iteration rule; (2) NMF is sensitive to noise (outliers), and thus the corrupted data will make the results of NMF meaningless. Although principal component analysis (PCA) can be used to mitigate these two problems, the transformed data will contain negative numbers, hindering the direct use of the multiplicative iteration rule of NMF. In this paper, we analyze the impact of PCA on NMF, and find that multiplicative NMF can also be applicable to data after principal component transformation. Based on this conclusion, we present a method to perform NMF in the principal component space, named ‘principal component NMF’ (PCNMF). Experimental results show that PCNMF is both accurate and time-saving.

关键词： Non-negative matrix factorization (NMF)     Principal component analysis (PCA)     Endmember     Hyperspectral    

HTML PDF 收藏

基于主成分分析法的秦巴山脉区域林业产业发展研究

郑东晖,后华,翟明普,渠美

《中国工程科学》 2020年 第22卷 第1期   页码 120-126 doi: 10.15302/J-SSCAE-2020.01.013

摘要：

林业产业作为国民经济的重要基础性产业，在生态建设中的地位日益突出。本文系统梳理了秦巴山脉区域的林业产业发展现状，详细开展了区域林业产业的优势、劣势、机遇和威胁（SWOT）分析，以专家问卷调查作为数据来源，运用主成分分析方法，以统计分析的方式寻找出对秦巴山脉林业产业发展有较大影响的要素，以之作为区域林业产业发展战略制定的科学依据。研究表明，生态环境问题、集约化程度低、森林旅游业发展机遇、区位优势、资源优势等方面是秦巴山脉区域林业产业发展的主要影响因素；区域林业产业发展重在于生态建设，应利用区位和资源优势，做大做强特色产业，优化产业结构；同时大力发展以森林旅游为主的第三产业，保障区域林业产业的高质量发展。

关键词： 秦巴山脉区域     林业产业     SWOT分析     主成分分析法     生态环境    

HTML PDF 收藏

AED-Net——异常事件检测网络 Article

王田, 苗子琛, 陈禹昕, 周毅, 单光存, Hichem Snoussi

《工程（英文）》 2019年 第5卷 第5期   页码 930-939 doi: 10.1016/j.eng.2019.02.008

摘要：

长期以来，在拥挤场景中检测异常事件都是一项具有挑战性的任务。为解决这一问题，本文提出了一种名叫异常事件检测网络（AED-Net）的自监督框架，它由主成分分析网络（PCANet）和核主成分分析（kPCA）组成。该框架以不同场景的监控视频序列为原始数据，通过训练PCANet以提取人群情况的高级语义。kPCA可作为一种单类分类器，被用于识别场景中的异常事件。与目前流行的一些深度学习方法相比，该框架完全是自监督的，因为它只使用正常情况下的视频序列。通过对明尼苏达大学公共监测人类活动数据集（UMN数据集）和加州大学圣地亚哥分校监测异常数据集（UCSD数据集）进行全局和局部异常事件进行检测发现，与其他最先进的方法相比，该方法具有更好的等误差率（EER）和曲线下面积（AUC）。此外，通过增加局部响应归一化（LRN）层，我们对原有的AED-Net进行了改进。结果表明，该改进版在提高框架的泛化能力方面表现出更好的性能。

关键词： 异常事件检测     异常事件检测网络     主成分分析网络     核主成分分析    

HTML PDF 收藏

基于主成分综合模型的矿区农田重金属污染评价

王从陆,吴超,段瑜

《中国工程科学》 2008年 第10卷 第7期   页码 180-183

摘要：

文章尝试利用变量聚类分析方法对矿区附近农田土壤重金属污染的主要污染物进行辨识，并采用 综合主成分分析法对矿区附近农田土壤重金属污染情况进行评价和分级。分析结果表明：利用变量聚类分 析法可以有效地辨识矿区附近农田土壤重金属污染中的主要成分；运用综合主成分分析法，确定样本的综合 主成分，并对其排序和聚类，可以有效揭示矿区附近农田土壤重金属污染物的数据结构、相互关系和不同样 品点的污染程度。采用主成分分析方法对矿区附近农田土壤重金属污染情况的评价结果，反映了矿区主要 重金属污染物的影响，同时又定量化了土壤复合重金属污染研究。辨识和评价结果可为矿区附近农田土壤 重金属污染治理对策的提出和重点治理区域的确定提供参考和指导。

关键词： 主成分综合模型     矿区农田土壤     重金属污染     评价    

HTML PDF 收藏

Indoor carbonyl compounds in an academic building in Beijing, China: concentrations and influencing factors

Chuanjia JIANG, Pengyi ZHANG

《环境科学与工程前沿（英文）》 2012年 第6卷 第2期   页码 184-194 doi: 10.1007/s11783-011-0309-3

摘要： Carbonyl compounds in indoor air are of great concern for their adverse health effects. Between February and May, 2009, concentrations of 13 carbonyl compounds were measured in an academic building in Beijing, China. Total concentration of the detected carbonyls ranged from 20.7 to 189.1 μg·m , and among them acetone and formaldehyde were the most abundant, with mean concentrations of 26.4 and 22.6 μg·m , respectively. Average indoor concentrations of other carbonyls were below 10 μg·m . Principal component analysis identified a combined effect of common indoor carbonyl sources and ventilation on indoor carbonyl levels. Diurnal variations of the carbonyl compounds were investigated in one office room, and carbonyl concentrations tended to be lower in the daytime than at night, due to enhanced ventilation. Average concentrations of carbonyl compounds in the office room were generally higher in early May than in late February, indicating the influence of temperature. Carbonyl source emission rates from both the room and human occupants were estimated during two lectures, based on one-compartment mass balance model. The influence of human occupants on indoor carbonyl concentrations varies with environmental conditions, and may become significant in the case of a large human occupancy.

关键词： carbonyl compounds     indoor air     ventilation     human occupancy     source emission rate (SER)     principal component analysis (PCA)    

HTML PDF 收藏

Indicating landfill stabilization state by using leachate property from Laogang Refuse Landfill

LOU Ziyang,CHAI Xiaoli,ZHAO Youcai,SONG Yu,ZHU Nanwen,JIA Jinping

《环境科学与工程前沿（英文）》 2014年 第8卷 第3期   页码 405-410 doi: 10.1007/s11783-013-0565-5

摘要： Variation and evolution process of leachate can be applied as a reference for landfill stabilization phase. In this work, leachates with different ages were collected from Laogang Refuse Landfill, and characterized with 14 key parameters. Simultaneously, principal component analysis (PCA) was applied to develop a synthetic parameter-F based on these 14 parameters, and a logarithm equation was simulated for the landfill stabilization process finally. It was predicted that leachates would meet Class I and Class II in standard for pollution control on the landfill site of municipal solid waste (GB 16889-1997) after 32 years and 22 years disposal under the natural attenuation in the humid and warm southern areas of China, respectively. The predication of landfill state would be more accurate and useful according to the synthetic parameter of leachate from a working landfill.

关键词： landfill stabilization     leachate evolution     principal component analysis    

HTML PDF 收藏

A dynamic stiffness-based framework for harmonic input estimation and response reconstruction considering damage

Yixian LI; Limin SUN; Wang ZHU; Wei ZHANG

《结构与土木工程前沿（英文）》 2022年 第16卷 第4期   页码 448-460 doi: 10.1007/s11709-022-0805-5

摘要： In structural health monitoring (SHM), the measurement is point-wise but structures are continuous. Thus, input estimation has become a hot research subject with which the full-field structural response can be calculated with a finite element model (FEM). This paper proposes a framework based on the dynamic stiffness theory, to estimate harmonic input, reconstruct responses, and to localize damages from seriously deficient measurements. To begin, Fourier transform converts the dynamic equilibrium equation to an equivalent static one in the frequency domain, which is under-determined since the dimension of measurement vector is far less than the FEM-node number. The principal component analysis has been adopted to “compress” the under-determined equation, and formed an over-determined equation to estimate the unknown input. Then, inverse Fourier transform converts the estimated input in the frequency domain to the time domain. Applying this to the FEM can reconstruct the target responses. If a structure is damaged, the estimated nodal force can localize the damage. To improve the damage-detection accuracy, a multi-measurement-based indicator has been proposed. Numerical simulations have validated that the proposed framework can capably estimate input and reconstruct multi-types of full-field responses, and the damage indicator can localize minor damages even with the existence of noise.

关键词： dynamic stiffness     principal component analysis     response reconstruction     damage localization     under-determined equation    

HTML PDF 收藏

Relationships of nitrous oxide fluxes with water quality parameters in free water surface constructed wetlands

Juan WU, Jian ZHANG, Wenlin JIA, Huijun XIE, Bo ZHANG

《环境科学与工程前沿（英文）》 2009年 第3卷 第2期   页码 241-247 doi: 10.1007/s11783-009-0023-6

摘要： The effects of chemical oxygen demand (COD) concentration in the influent on nitrous oxide (N O) emissions, together with the relationships between N O and water quality parameters in free water surface constructed wetlands, were investigated with laboratory-scale systems. N O emission and purification performance of wastewater were very strongly dependent on COD concentration in the influent, and the total N O emission in the system with middle COD influent concentration was the least. The relationships between N O and the chemical and physical water quality variables were studied by using principal component scores in multiple linear regression analysis to predict N O flux. The multiple linear regression model against principal components indicated that different water parameters affected N O flux with different COD concentrations in the influent, but nitrate nitrogen affected N O flux in all systems.

关键词： free water surface constructed wetland     nitrous oxide emission     water quality parameter     principal component analysis     multiple linear regression    

HTML PDF 收藏

Determination of the principal factors of river water quality through cluster analysis method and its

Liang GUO, Ying ZHAO, Peng WANG

《环境科学与工程前沿（英文）》 2012年 第6卷 第2期   页码 238-245 doi: 10.1007/s11783-011-0382-7

摘要： In this paper, an artificial neural network model was built to predict the Chemical Oxygen Demand (COD ) measured by permanganate index in Songhua River. To enhance the prediction accuracy, principal factors were determined through the analysis of the weight relation between influencing factors and forecasting object using cluster analysis method, which optimized the topological structure of the prediction model input items of the artificial neural network. It was shown that application of the principal factors in water quality prediction model can improve its forecasting skill significantly through the comparison between results of prediction by artificial neural network and the measurements of the COD . This methodology is also applicable to various water quality prediction targets of other water bodies and it is valuable for theoretical study and practical application.

关键词： water quality forecast     principal factor     cluster analysis method     artificial neural network    

HTML PDF 收藏

Regional wind power forecasting model with NWP grid data optimized

Zhao WANG, Weisheng WANG, Bo WANG

《能源前沿（英文）》 2017年 第11卷 第2期   页码 175-183 doi: 10.1007/s11708-017-0471-9

摘要： Unlike the traditional fossil energy, wind, as the clean renewable energy, can reduce the emission of the greenhouse gas. To take full advantage of the environmental benefits of wind energy, wind power forecasting has to be studied to overcome the troubles brought by the variable nature of wind. Power forecasting for regional wind farm groups is the problem that many power system operators care about. The high-dimensional feature sets with redundant information are frequently encountered when dealing with this problem. In this paper, two kinds of feature set construction methods are proposed which can achieve the proper feature set either by selecting the subsets or by transforming the original variables with specific combinations. The former method selects the subset according to the criterion of minimal-redundancy-maximal-relevance (mRMR), while the latter does so based on the method of principal component analysis (PCA). A locally weighted learning method is also proposed to utilize the processed feature set to produce the power forecast results. The proposed model is simple and easy to use with parameters optimized automatically. Finally, a case study of 28 wind farms in East China is provided to verify the effectiveness of the proposed method.

关键词： regional wind power forecasting     feature set     minimal-redundancy-maximal-relevance (mRMR)     principal component analysis (PCA)     locally weighted learning model    

HTML PDF 收藏

Predicting non-carcinogenic hazard quotients of heavy metals in pepper (

Marzieh Mokarram, Hamid Reza Pourghasemi, Huichun Zhang

《环境科学与工程前沿（英文）》 2020年 第14卷 第6期 doi: 10.1007/s11783-020-1331-0

摘要： Abstract • There was significant absorption of heavy metals by the pepper in contaminated soils. • The target hazard quotient (THQ) indices followed the order of Pb>Zn>>Cd » Ni. • Relationships exist between contaminated plants and electromagnetic wave. • PCA and random search can select the main spectra and predict THQ for each element. Given the tendency of heavy metals to accumulate in soil and plants, the purpose of this study was to determine the contamination levels of Cd, Ni, Pb, and Zn on peppers (leaves and fruit) grown in contaminated soils in industrial centers. For this purpose, we measured the uptake of the four heavy metals by peppers grown in the heavy metal contaminated soils throughout the four growth stages: two-leaf, growth, flowering, and fruiting, and calculated various vegetation indices to evaluate the heavy metal contamination potentials. Electromagnetic waves were also applied for analyzing the responses of the target plants to various heavy metals. Based on the relevant spectral bands identified by principal component analysis (PCA) and random search methods, a regression method was then employed to determine the most optimal spectral bands for estimating the target hazard quotient (THQ). The THQ was found to be the highest in the plants contaminated by Pb (THQ= 62) and Zn (THQ= 5.07). The results of PCA and random search indicated that the spectra at the bands of b570, b650, and b760 for Pb, b400 and b1030 for Ni, b400 and b880 for Cd, and b560, b910, and b1050 for Zn were the most optimal spectra for assessing THQ. Therefore, in future studies, instead of examining the amount of heavy metals in plants by chemical analysis in the laboratory, the responses of the plants to the electromagnetic waves in the identified bands can be readily investigated in the field based on the established correlations.

关键词： Heavy metals     Plants     Target Hazard Quotient (THQ)     Principal Component Analysis (PCA)     Random search     Electromagnetic wave    

HTML PDF 收藏

Interaction and independence on methane oxidation of landfill cover soil among three impact factors: water, oxygen and ammonium

Pinjing HE, Na YANG, Wenjuan FANG, Fan Lü, Liming SHAO

《环境科学与工程前沿（英文）》 2011年 第5卷 第2期   页码 175-185 doi: 10.1007/s11783-011-0320-8

摘要： To understand the influence patterns and interactions of three important environmental factors, i.e. soil water content, oxygen concentration, and ammonium addition, on methane oxidation, the soils from landfill cover layers were incubated under full factorial parameter settings. In addition to the methane oxidation rate, the quantities and community structures of methanotrophs were analyzed to determine the methane oxidation capacity of the soils. Canonical correspondence analysis was utilized to distinguish the important impact factors. Water content was found to be the most important factor influencing the methane oxidation rate and Type II methanotrophs, and the optimum value was 15% (w/w), which induced methane oxidation rates 10- and 6- times greater than those observed at 5% (w/w) and 20% (w/w), respectively. Ambient oxygen conditions were more suitable for methane oxidation than 3% oxygen. The addition of of ammonium induced different effects on methane oxidation capacity when conducted at low or high water content. With regard to the methanotrophs, Type II was sensitive to the changes of water content, while Type I was influenced by oxygen content. Furthermore, the methanotrophic acidophile, , was detected in soils with a pH of 4.9, which extended their known living environments.

关键词： quantitative polymerase chain reaction (PCR)     denaturing gradient gel electrophoresis (DGGE)     principal component analysis (PCA)     canonical correspondence analysis (CCA)    

HTML PDF 收藏

Static-based early-damage detection using symbolic data analysis and unsupervised learning methods

João Pedro SANTOS,Christian CREMONA,André D. ORCESI,Paulo SILVEIRA,Luis CALADO

《结构与土木工程前沿（英文）》 2015年 第9卷 第1期   页码 1-16 doi: 10.1007/s11709-014-0277-3

摘要： A large amount of researches and studies have been recently performed by applying statistical and machine learning techniques for vibration-based damage detection. However, the global character inherent to the limited number of modal properties issued from operational modal analysis may be not appropriate for early-damage, which has generally a local character. The present paper aims at detecting this type of damage by using static SHM data and by assuming that early-damage produces dead load redistribution. To achieve this objective a data driven strategy is proposed, consisting of the combination of advanced statistical and machine learning methods such as principal component analysis, symbolic data analysis and cluster analysis. From this analysis it was observed that, under the noise levels measured on site, the proposed strategy is able to automatically detect stiffness reduction in stay cables reaching at least 1%.

关键词： structural health monitoring     early-damage detection     principal component analysis     symbolic data     symbolic dissimilarity measures     cluster analysis     numerical model     damage simulations    

HTML PDF 收藏

A systematic approach in load disaggregation utilizing a multi-stage classification algorithm for consumer electrical appliances classification

Chuan Choong YANG, Chit Siang SOH, Vooi Voon YAP

《能源前沿（英文）》 2019年 第13卷 第2期   页码 386-398 doi: 10.1007/s11708-017-0497-z

摘要： The potential to save energy in existing consumer electrical appliances is very high. One of the ways to achieve energy saving and improve energy use awareness is to recognize the energy consumption of individual electrical appliances. To recognize the energy consumption of consumer electrical appliances, the load disaggregation methodology is utilized. Non-intrusive appliance load monitoring (NIALM) is a load disaggregation methodology that disaggregates the sum of power consumption in a single point into the power consumption of individual electrical appliances. In this study, load disaggregation is performed through voltage and current waveform, known as the - trajectory. The classification algorithm performs cropping and image pyramid reduction of the - trajectory plot template images before utilizing the principal component analysis (PCA) and the -nearest neighbor ( -NN) algorithm. The novelty of this paper is to establish a systematic approach of load disaggregation through - trajectory-based load signature images by utilizing a multi-stage classification algorithm methodology. The contribution of this paper is in utilizing the “ -value,” the number of closest data points to the nearest neighbor, in the -NN algorithm to be effective in classification of electrical appliances. The results of the multi-stage classification algorithm implementation have been discussed and the idea on future work has also been proposed.

关键词： load disaggregation     voltage-current (V-I) trajectory     multi-stage classification algorithm     principal component analysis (PCA)     k-nearest neighbor (k-NN)    

HTML PDF 收藏

标题 作者 时间 类型 操作

Multi-objective optimization of cutting parameters in high-speed milling based on grey relational analysiscoupled with principal component analysis

Tao FU, Jibin ZHAO, Weijun LIU

期刊论文

Non-negativematrix factorization based unmixing for principal component transformed hyperspectral data

Xiu-rui GENG,Lu-yan JI,Kang SUN

期刊论文

基于主成分分析法的秦巴山脉区域林业产业发展研究

郑东晖,后华,翟明普,渠美

期刊论文

AED-Net——异常事件检测网络

王田, 苗子琛, 陈禹昕, 周毅, 单光存, Hichem Snoussi

期刊论文

基于主成分综合模型的矿区农田重金属污染评价

王从陆,吴超,段瑜

期刊论文

Indoor carbonyl compounds in an academic building in Beijing, China: concentrations and influencing factors

Chuanjia JIANG, Pengyi ZHANG

期刊论文

Indicating landfill stabilization state by using leachate property from Laogang Refuse Landfill

LOU Ziyang,CHAI Xiaoli,ZHAO Youcai,SONG Yu,ZHU Nanwen,JIA Jinping

期刊论文

A dynamic stiffness-based framework for harmonic input estimation and response reconstruction considering damage

Yixian LI; Limin SUN; Wang ZHU; Wei ZHANG

期刊论文

Relationships of nitrous oxide fluxes with water quality parameters in free water surface constructed wetlands

Juan WU, Jian ZHANG, Wenlin JIA, Huijun XIE, Bo ZHANG

期刊论文

Determination of the principal factors of river water quality through cluster analysis method and its

Liang GUO, Ying ZHAO, Peng WANG

期刊论文

Regional wind power forecasting model with NWP grid data optimized

Zhao WANG, Weisheng WANG, Bo WANG

期刊论文

Predicting non-carcinogenic hazard quotients of heavy metals in pepper (

Marzieh Mokarram, Hamid Reza Pourghasemi, Huichun Zhang

期刊论文

Interaction and independence on methane oxidation of landfill cover soil among three impact factors: water, oxygen and ammonium

Pinjing HE, Na YANG, Wenjuan FANG, Fan Lü, Liming SHAO

期刊论文

Static-based early-damage detection using symbolic data analysis and unsupervised learning methods

João Pedro SANTOS,Christian CREMONA,André D. ORCESI,Paulo SILVEIRA,Luis CALADO

期刊论文

A systematic approach in load disaggregation utilizing a multi-stage classification algorithm for consumer electrical appliances classification

Chuan Choong YANG, Chit Siang SOH, Vooi Voon YAP

期刊论文