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Knowledge-based System Prototype in Structural component Design Based on FM

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哈尔滨工程大学学报（英文版） 2002年第2期1卷 66-71页

作者： JIANG Tao ,LI Qing-fen ,LI Ming ,FU Wei1. Power Generation Department, Luneng Company, Jinan 250001, China2. College of Mechanical and Electrical Engineering, Harbin Engineering University,Harbin 150001, China3. Technique Superintendence Center, Daqing Oil Field Company Ltd, Daqing 163453, China Luneng Company Power Generation Department Jinan China Harbin Engineering University College of Mechanical and Electrical Engineering Harbin China (GRID:grid.33764.35) (ISNI:***) Daqing Oil Field Company Ltd Technique Superintendence Center Daqing China (GRID:grid.33764.35)

A knowledge-based system in structural component design based on fracture mechanics is developed in this paper. The system consists of several functional parts: a general inference engine, a set of knowledge bases and data-bases, an interpretation engine, a bases administration system and the interface. It can simulate a human expert to make analysis and design scheme mainly for four kinds of typical structural components widely used in shipbuilding industry: pressure vessels, huge rotation constructions, pump-rod and welded structures. It is an open system which may be broadened and perfected to cover a wider range of engineering application through the modification and enlargement of knowledge bases and data-bases. It has a natural and friendly interface that may be easily operated. An on-line help service is also provided.

关键词： Expert system Fracture Mechanics Structural component design

Loss-of-Main Monitoring and Detection for Distributed Generations Using Dynamic Principal component Analysis

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Journal of Power and Energy Engineering 2014年第4期2卷 423-431页

作者： Yuanjun Guo Kang Li D. M. Laverty School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast Belfast UK

In this paper, our previous work on Principal component Analysis (PCA) based fault detection method is extended to the dynamic monitoring and detection of loss-of-main in power systems using wide-area synchrophasor measurements. In the previous work, a static PCA model was built and verified to be capable of detecting and extracting system faulty events;however the false alarm rate is high. To address this problem, this paper uses a well-known ‘time lag shift’ method to include dynamic behavior of the PCA model based on the synchronized measurements from Phasor Measurement Units (PMU), which is named as the Dynamic Principal component Analysis (DPCA). Compared with the static PCA approach as well as the traditional passive mechanisms of loss-of-main detection, the proposed DPCA procedure describes how the synchrophasors are linearly auto- and cross-correlated, based on conducting the singular value decomposition on the augmented time lagged synchrophasor matrix. Similar to the static PCA method, two statistics, namely T2 and Q with confidence limits are calculated to form intuitive charts for engineers or operators to monitor the loss-of-main situation in real time. The effectiveness of the proposed methodology is evaluated on the loss-of-main monitoring of a real system, where the historic data are recorded from PMUs installed in several locations in the UK/Ireland power system.

关键词： Loss-of-Main Detection Phasor Measurement Units Big Data Dynamic Principal component Analysis

FAST RECURSIVE LEAST SQUARES LEARNING ALGORITHM FOR PRINCIPAL component ANALYSIS

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Journal of Electronics(China) 2000年第3期17卷 270-278页

作者： Ouyang Shan Bao Zheng Liao Guisheng(Guilin Institute of Electronic Technology, Guilin 541004)(Key Laboratory of Radar Signal Processing, Xidian Univ., Xi’an 710071) Guilin Institute of Electronic Technology Guilin Key Laboratory of Radar Signal Processing Xidian Univ. Xi’an

Based on the least-square minimization a computationally efficient learning algorithm for the Principal component Analysis(PCA) is derived. The dual learning rate parameters are adaptively introduced to make the proposed algorithm providing the capability of the fast convergence and high accuracy for extracting all the principal components. It is shown that all the information needed for PCA can be completely represented by the unnormalized weight vector which is updated based only on the corresponding neuron input-output product. The convergence performance of the proposed algorithm is briefly *** relation between Oja’s rule and the least squares learning rule is also established. Finally, a simulation example is given to illustrate the effectiveness of this algorithm for PCA.

关键词： Neural networks Principal component analysis Auto-association Recursive least squares(RLS) learning rule

Identification of essential language areas by combination of fMRI from different tasks using probabilistic independent component analysis

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Journal of Biomedical Science and Engineering 2008年第3期1卷 157-162页

作者： Yanmei Tie Ralph O. Suarez Stephen Whalen Isaiah H. Norton Alexandra J. Golby Departments of Neurosurgery and Radiology Brigham and Women’s Hospital Harvard Medical School Boston MA USA epartments of Neurosurgery and Radiology Brigham and Women’s Hospital Harvard Medical School Boston MA USA

Functional magnetic resonance imaging (fMRI) has been used to lateralize and localize lan-guage areas for pre-operative planning pur-poses. To identify the essential language areas from this kind of observation method, we pro-pose an analysis strategy to combine fMRI data from two different tasks using probabilistic in-dependent component analysis (PICA). The assumption is that the independent compo-nents separated by PICA identify the networks activated by both tasks. The results from a study of twelve normal subjects showed that a language-specific component was consistently identified, with the participating networks sepa-rated into different components. Compared with a model-based method, PICA’s ability to capture the neural networks whose temporal activity may deviate from the task timing suggests that PICA may be more appropriate for analyzing language fMRI data with complex event-related paradigms, and may be particularly helpful for patient studies. This proposed strategy has the potential to improve the correlation between fMRI and invasive techniques which can dem-onstrate essential areas and which remain the clinical gold standard.

关键词： fMRI probabilistic independent component analysis (PICA) language mapping event-related paradigm

The Water Quality Evaluation in Balihe Lake Based on Principal component Analysis

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Journal of Geoscience and Environment Protection 2019年第8期7卷 38-48页

作者： Lei Zhang Meifang Zhong Yixin Xu Zhongyu Wang Hai Huang Research Center for Engineering Ecology and Nonlinear Science North China Electric Power University Beijing China

The water pollution situation in Balihe Lake, the biggest tributary of Shaying River Basin in Anhui Province, China, has brought a huge pressure on the improvement of water quality in Huai River. On October 16th, 2017, 11 major pollution indexes were observed at 15 sampling points in Balihe Lake. Based on the data experimentally measured, the water quality in Balihe Lake was analyzed utilizing the Principal component Analysis (PCA) of SPSS. The result suggested that the major components were oxygenated pollutants, water eutrophication pollutants and ammonia nitrogen, in which oxygenated pollutants played a dominant role. In addition, the upper part of Balihe Lake suffered serious situation and needed a focus on oxygenated pollutants.

关键词： Balihe Lake Water Quality Principal component Analysis Environmental Protection

Rapid Algorithm for Independent component Analysis

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Journal of Signal and Information Processing 2012年第3期3卷 275-285页

作者： Ryota Yokote Yasuo Matsuyama Waseda University

A class of rapid algorithms for independent component analysis (ICA) is presented. This method utilizes multi-step past information with respect to an existing fixed-point style for increasing the non-Gaussianity. This can be viewed as the addition of a variable-size momentum term. The use of past information comes from the idea of surrogate optimization. There is little additional cost for either software design or runtime execution when past information is included. The speed of the algorithm is evaluated on both simulated and real-world data. The real-world data includes color images and electroencephalograms (EEGs), which are an important source of data on human-computer interactions. From these experiments, it is found that the method we present here, the RapidICA, performs quickly, especially for the demixing of super-Gaussian signals.

关键词： Independent component Analysis Speedup Past Information Momentum Super-Gaussian Negentropy

Seismic Data Quality Control and Interpolation Using Principal component Analysis

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International Journal of Geosciences 2019年第10期10卷 950-966页

作者： Qingmou Li Sonya A. Dehler Natural Resources Canada Canadian Hazards Information Service (CHIS) Ottawa ON Canada Natural Resources Canada Geological Survey of Canada (Calgary) Calgary Alberta Canada.

Commonly, seismic data processing procedures, such as stacking and prestack migration, require the ability to detect bad traces/shots and restore or replace them by interpolation, particularly when the seismic observations are noisy or there are malfunctioned components in the recording system. However, currently available trace/shot interpolation methods in the spatial or Fourier domain must deal with requirements such as evenly sampled traces/shots, infinite bandwidth of the signals, and linear seismic events. In this paper, we present a novel method, termed the E-S (eigenspace seismic) method, using principal component analysis (PCA) of the seismic signal to address the issue of reliable detection or interpolation of bad traces/shots. The E-S method assumes the existence of a correlation between the observed seismic entities, such as trace or shot gathers, making it possible to estimate one of these entities from all others for interpolation or seismic quality control. It first transforms a trace (or shot) gather into an eigenspace using PCA. Then in the eigenspace, it treats every trace as a point with its loading scores of PCA as its coordinates. Simple linear, bilinear, or cubic spline 1 dimensional (1D) interpolation is used to determine PCA loading scores for any arbitrary coordinate in the eigenspace, which are then used to construct an interpolated trace for the desired position in physical space. This E-S method works with either regular or irregular sampling and, unlike various other published methods, it is well-suited for band-limited seismic records with curvilinear reflection events. We developed related algorithms and applied these to processed synthetic and offshore seismic survey data with or without simulated noises to demonstrate their performance. By comparing the interpolated and observed seismic traces, we find that the E-S method can effectively assess the quality of the trace, and restore poor quality data by interpolation. The successful p

关键词： Seismic Quality Control Seismic Interpolation Eigenspace Principal component Analysis

A component-BASED MODELING AND SIMULATION SUPPORTING ENVIRONMENT

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International Journal of Modeling, Simulation, and Scientific Computing 2013年第4期4卷 73-84页

作者： TENG DA SONG XIAO Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical Engineering Beihang University BeijingP.R.China

Simulation modelers,engineers and managers are faced with new challenges at large-scale complex simulation application *** reduce the difficulty of developing such simulation applications,the simulation environment is required to be extensible,reusable and *** order to promote the reusability from coarse-grained federate to fine-grained components,this paper proposes a modeling and simulation environment which consists of component-based architecture,modeling methods,and simulation services to support and simplify the process of complex simulation application ***,a standard process and simulation tools are developed to ensure the rapid and effective development of simulation applications.

关键词： Simulation framework HLA component

The Principal component Transform of Parametrized Functions

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Applied Mathematics 2017年第4期8卷 453-475页

作者： Ilia Zabrodskii Arcady Ponosov Department of Science and Technology Norwegian University of Life Sciences &Aring s Norway

Many advanced mathematical models of biochemical, biophysical and other processes in systems biology can be described by parametrized systems of nonlinear differential equations. Due to complexity of the models, a problem of their simplification has become of great importance. In particular, rather challengeable methods of estimation of parameters in these models may require such simplifications. The paper offers a practical way of constructing approximations of nonlinearly parametrized functions by linearly parametrized ones. As the idea of such approximations goes back to Principal component Analysis, we call the corresponding transformation Principal component Transform. We show that this transform possesses the best individual fit property, in the sense that the corresponding approximations preserve most information (in some sense) about the original function. It is also demonstrated how one can estimate the error between the given function and its approximations. In addition, we apply the theory of tensor products of compact operators in Hilbert spaces to justify our method for the case of the products of parametrized functions. Finally, we provide several examples, which are of relevance for systems biology.

关键词： Principal component Analysis Discretization of Functions Metamodeling Latent Parameters