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Assessment of Long-Term Physicochemical Water Quality Variations by pca Technique in Lake Hwajinpo, South Korea

Journal of Environmental Protection 2017年第13期8卷 1636-1651页

作者： Bal Dev Bhattrai Sungjin Kwak Kwangsoon Choi Woomyung Heo Department of Earth and Environmental Engineering Kangwon National University Samcheok Republic of Korea K-Water Institute Korea Water Resources Corporation Daejeon Republic of Korea

Physicochemical properties of water were analyzed to assess the long-term water quality variations in Lake Hwajinpo, Korea. Water quality data monitored from 1998 to 2015 was divided in three periods and descriptive statistics, correlation and rotated components were deducted using statistical procedures. Based on the results of analyses, water quality patterns in three periods (1998-2003, 2004-2009 and 2013-2015) were distinguishable from each other. Water parameters, Chlorophyll-a, total phosphorus, total nitrogen, dissolved oxygen and pH, showed the highest mean values in the first period. On the other hand, conductivity and salinity in the second period and temperature and suspended solids in the third period showed the highest mean values. Principal component analysis (pca) procedure was utilized to deduct the most significant parameters influencing water quality and observed that each period had different pattern of variables. Salinity and conductivity were the two variables highly contributing in first component/factor (F1) explaining 20.77% and 22.93% of the total variance in the first period and second period, respectively. But total phosphorus and chlorophyll-a were the two variables highly loading in F1 of the third period explaining 23.72% of total variance. These results revealed that the water quality of Lake Hwajinpo had different patterns of variations throughout the study period. Thus, pca results could be valuable to understand the water quality status of water body and take proper steps to protect the water environment.

关键词： Physicochemical Parameters Coastal Lagoon pca Lake Hwajinpo

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pca与KLE相结合的区域GPS网坐标序列分析

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测绘科学 2014年第7期39卷 113-117,97页

作者：贺小星花向红周世健田茂武汉大学测绘学院武汉430079 武汉大学灾害监测与防治研究中心武汉430079 东华理工大学测绘工程学院南昌330013 江西省科学院南昌330029

对于GPS区域网坐标序列中普遍存在的共模误差(CME),常规方法是利用堆栈空间滤波去除,通常假设CME是空域不变的;而主成分分析法(pca)和Karhunen-Loeve(KLE)展开法都是把随时间变化的台站网时间序列分解成时间域的主分量和空间域的特征分... 详细信息

对于GPS区域网坐标序列中普遍存在的共模误差(CME),常规方法是利用堆栈空间滤波去除,通常假设CME是空域不变的;而主成分分析法(pca)和Karhunen-Loeve(KLE)展开法都是把随时间变化的台站网时间序列分解成时间域的主分量和空间域的特征分量,不限制CME的本征。因此,本文尝试结合pca和KLE方法对GPS区域网坐标序列进行空间滤波,并通过对美国中加州Carrizo平原的一个连续运行GPS监测网进行分析,表明pca/KLE法可有效提取共模误差,提高站点坐标精度,增强空间滤波的稳健性。

关键词： GPS坐标时间序列共模误差 pca KLE 空间滤波

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pca Application in Channel Estimation in MIMO-OFDM System

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International Journal of Communications, Network and System Sciences 2011年第6期4卷 384-387页

作者： Mona Nasseri Hamidreza Bakhshi Sara Sahebdel Razieh Falahian Maryam Ahmadi 不详

Initial estimation is a considerable issue in channel estimation techniques, since all of the following processes depends on it, which in this paper its improvement is discussed. Least Square (LS) method is a common simple way to estimate a channel initially but its efficiency is not as significant as more complex approaches. It is possible to enhance channel estimation performance by using some methods such as principal component analysis (pca), which is not prevalent in channel estimation, and its adaptation to channel information can be challenging. pca method improves initial estimation performance by projecting data onto direction of eigenvectors by means of using simple algebra. In this paper, channel estimation is examined in Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system, with significant advantages such as an acceptable performance in frequency selective fading channel. Moreover the proposed channel estimation method manipulates the benefits of MIMO channel by using the information, gained by all channels to estimate the information of each receiver.

关键词： Channel Estimation Mimo-Ofdm pca Pilot Aided Technique

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基于pca的概率神经网络模式分类方法

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北京理工大学学报 1996年第1期16卷 69-74页

作者：黄德双北京理工大学电子工程系

研究了概率神经网络隐单元主要分量的选取方法（特征分解法、正交迭代法和于空间学习法）．这些方法构成的概率神经网络比原来的网络大大降低了隐单元数，并且带来分类测试时间减少的增益．最后，就模拟和实测两组数据进行了计算机仿真... 详细信息

研究了概率神经网络隐单元主要分量的选取方法（特征分解法、正交迭代法和于空间学习法）．这些方法构成的概率神经网络比原来的网络大大降低了隐单元数，并且带来分类测试时间减少的增益．最后，就模拟和实测两组数据进行了计算机仿真，实验结果证实了这种方法的可行性．

关键词：雷达目标概率神经网络模式分类 pca 模式识别

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基于pca-EDT-CNN的柴油发动机故障诊断方法

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机械强度 2022年第6期44卷 1271-1278页

作者：白雲杰贾希胜梁庆海马云飞温亮陆军工程大学石家庄校区石家庄050003 河北省机械装备状态监测与评估重点实验室石家庄050003

针对柴油发动机故障诊断中振动信号呈非平稳、非线性,直接将原始信号输入卷积神经网络(Convolutional Neural Network, CNN)进行故障诊断效果差的问题,提出一种基于pca-EDT-CNN的新方法。首先,利用主成分分析(Principal Component Analy... 详细信息

针对柴油发动机故障诊断中振动信号呈非平稳、非线性,直接将原始信号输入卷积神经网络(Convolutional Neural Network, CNN)进行故障诊断效果差的问题,提出一种基于pca-EDT-CNN的新方法。首先,利用主成分分析(Principal Component Analysis, pca)对传感器采集的原始数据进行自适应降维,并构建符合条件的主成分特征向量矩阵(Principal Component Eigenvector Matrix, PCEM);其次,对PCEM进行欧氏距离变换(Euclidean Distance Transformation, EDT),计算各行间欧氏距离并构建欧氏距离矩阵(Euclidean Distance Matrix, EDM);最后,将PCEM和EDM分别展平为一维向量并合成一个一维样本序列,输入到一维卷积神经网络(One-Dimensional Convolutional Neural Network, 1DCNN)对模型进行训练与诊断。搭建柴油发动机预置故障试验台,验证了该方法的有效性,且通过与传统方法对比,结果表明,该方法对柴油发动机不同故障状态诊断准确率高,具有实际工程应用价值。

关键词：一维卷积神经网络 pca EDT 柴油发动机故障诊断

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Spatial Variability of Ground Water Quality Using HCA, pca and MANOVA at Lawspet, Puducherry in India

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Computational Water, Energy, and Environmental Engineering 2017年第3期6卷 243-268页

作者： N. Suresh Nathan R. Saravanane T. Sundararajan Department of Civil Engineering Pondicherry Engineering College Puducherry India

In ground water quality studies multivariate statistical techniques like Hierarchical Cluster Analysis (HCA), Principal Component Analysis (pca), Factor Analysis (FA) and Multivariate Analysis of Variance (MANOVA) were employed to evaluate the principal factors and mechanisms governing the spatial variations and to assess source apportionment at Lawspet area in Puducherry, India. pca/FA has made the first known factor which showed the anthropogenic impact on ground water quality and this dominant factor explained 82.79% of the total variance. The other four factors identified geogenic and hardness components. The distribution of first factor scores portray high loading for EC, TDS, Na+ and Cl−(anthropogenic) in south east and south west parts of the study area, whereas other factor scores depict high loading for HCO3−, Mg2+, Ca2+ and TH (hardness and geogenic) in the north west and south west parts of the study area. K+ and SO42−(geogenic) are dominant in south eastern direction. Further MANOVA showed that there are significant differences between ground water quality parameters. The spatial distribution maps of water quality parameters have rendered a powerful and practical visual tool for defining, interpreting, and distinguishing the anthropogenic, hardness and geogenic factors in the study area. Further the study indicated that multivariate statistical methods have successfully assessed the ground water qualitatively and spatially with a more effective step towards ground water quality management.

关键词： HCA pca FA MANOVA Spatial Variability Puducherry India

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基于ANN和pca的多牌号产品生产过程故障检测及诊断

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计算机与应用化学 2010年第5期27卷 703-708页

作者：李文科钟向宏赵劲松清华大学化学工程系北京100084 中国石化集团茂名石油化工公司广东茂名525000

多牌号产品生产过程经常涉及到牌号切换,而切换后新牌号生产过程的变量关系可能随之发生变化,故采用单一的故障检测和诊断方法,无法对多牌号产品连续生产过程出现的异常做出有效的判断。这就需要及时准确地识别出新牌号,并对每个牌号有... 详细信息

多牌号产品生产过程经常涉及到牌号切换,而切换后新牌号生产过程的变量关系可能随之发生变化,故采用单一的故障检测和诊断方法,无法对多牌号产品连续生产过程出现的异常做出有效的判断。这就需要及时准确地识别出新牌号,并对每个牌号有相应的故障检测和诊断模型。为此,本文引入人工神经网络(ANN),将其用于牌号识别,提出了牌号识别和主成分分析(pca)相结合的方法,即利用历史数据建立各个牌号的BP神经网络(BPNN)模型和pca模型,在线数据经过BPNN识别确认牌号类型后,调用对应牌号的pca模型进行故障检测和诊断。结果表明,BPNN不仅可以准确识别牌号,识别率较规格界限法更高,而且可以对牌号过渡过程进行判断。另外,与不进行牌号识别仅采用单一牌号正常样本或者所有牌号正常样本混合建立的pca模型相比较,采用牌号识别后进行故障检测时的精度更高,证明了该方法的有效性。

关键词：神经网络 pca 牌号过渡牌号识别故障诊断

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Reconsiderations on the use of pipeline embolization device in the treatment of intracerebral aneurysms with special angioarchitecture: fetal pca, AVM, V-B junction and DAVF

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Chinese Neurosurgical Journal 2018年第4期4卷 210-218页

作者： Yupeng Zhang Peng Yan Yuntao Di Fei Liang Yuxiang Zhang Shikai Liang Chuhan Jiang Beijing Neurosurgical Institute and Beijing Tiantan Hospital Capital Medical UniversityBeijing 100050China Department of Neurosurgery The People’s Hospital of Tangxian CountyTangshanHebeiChina Department of Neurosurgery Beijing Tsinghua Changgung HospitalTsinghua UniversityBeijingChina

Background: Pipeline embolization device (PED) has proved its safety and efficacy in the treatment of intracranial large and giant side-wall aneurysms. With the accumulation of treatment experience, it is an inevitable trend to expand its off-label use on aneurysms. Whether flow diversion is safe and efficient in cases with special angioarchitecture has rarely been explored. Methods: We performed a retrospective analysis of 210 consecutive patients treated by PED for intracerebral aneurysms in our center. Except for aneurysm, those patients also presented with special angioarchitecture:Fetal pca, AVM, V-B junction and DAVF. Results: Nine patients were qualified for the study. 1 was with fetal pca, the aneurysm remained patent on 4-month follow-up. 2 with ipsilateral AVMs, one patient died due to brain hemorrhage 20 days after the operation, the other one was only partially embolised on 6 month follow up. 3 aneurysms located at V-B junction, angiographic follow up on 3 months demonstrated no complete occlusion of both the aneurysms, the other patients were still on follow up. All of the 3 cases with concomitant DAVF are completely occluded during short to midterm follow up. Conclusions: PED for aneurysms incorporated the fetal pca and V-B junction might meet a high propensity for incomplete occlusion during short term follow up. Aneurysm with ipsilateral AVM is not suitable for PED treatment due to the risk of hemorrhage and incomplete occlusion during midterm follow up. For aneurysm with concurrent DAVF, PED treatment is safe and efficient relatively in one session or by staged operation.

关键词： PED Aneurysm AVM Fetal pca V-B junction DAVF

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Improved pca Face Recognition Algorithm

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国际计算机前沿大会会议论文集 2020年第1期 602-611页

作者： Yang Tao Yuanzi He Nanchang Institute of Science and Technology NanchangChina Guangdong University of Science and Technology DongguanChina

Face recognition technology,as a biometric recognition technology,is very mature and has many *** achieves in-depth applications in smart campus systems,such as classroom attendance,classroom behavior analysis,and smart *** human faces as the face data foundation,computer vision and image processing technologies are applied to research and implement face *** on the principal component analysis(pca)theory,this paper analyzed the characteristics of face data,studied the face recognition *** the LBP and SVM algorithm,an improved pca face recognition algorithm was *** comparative experiments,the results show that the proposed algorithm can improve the accuracy of face recognition.

关键词： LBP pca SVM Face recognition

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Product Quality Prediction by a Neural Soft-Sensor Based on MSA and pca

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International Journal of Automation and computing 2006年第1期3卷 17-22页

作者： Jian Shi Xing-Gao Liu National Laboratory of Industrial Control Technology Institute of Systems Enginccring Zhejiang University Hangzhou 310027 PRC

A novel soft-sensor model which incorporates pca （principal component analysis）, RBF （Radial Basis Function） networks, and MSA （Multi-scale analysis）, is proposed to infer the properties of manufactured products from real process variables. pca is carried out to select the most relevant process features and to eliminate the correlations of input variables; multi-scale analysis is introduced to acquire much more information and to reduce uncertainty in the system; and RBF networks are used to characterize the nonlinearity of the process. A prediction of the melt index （MI）, or quality of polypropylene produced in an actual industrial process, is taken as a case study. Research results show that the proposed method provides promising prediction reliability and accuracy.

关键词： pca RBF MSA Polypropylene MI.

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