Failure Mode Recognition Clustering Algorithm Based on Manifold Learning

2012 ◽  
Vol 263-266 ◽  
pp. 2126-2130 ◽  
Author(s):  
Zhi Gang Lou ◽  
Hong Zhao Liu
Keyword(s):  
Manifold Learning ◽  
Clustering Algorithm ◽  
Learning Algorithm ◽  
Spectral Characteristics ◽  
Normal Operation ◽  
High Dimensional ◽  
Dimensional Manifold ◽  
Pattern Clustering ◽  
Oil Pipeline ◽  
Adjustable Valve

Manifold learning is a new unsupervised learning method. Its main purpose is to find the inherent law of generated data sets. Be used for high dimensional nonlinear fault samples for learning, in order to identify embedded in high dimensional data space in the low dimensional manifold, can be effective data found the essential characteristics of fault identification. In many types of fault, sometimes often failure and normal operation of the equipment of some operation similar to misjudgment, such as oil pipeline transportation process, pipeline regulating pump, adjustable valve, pump switch, normal operation and pipeline leakage fault condition similar spectral characteristics, thus easy for pipeline leakage cause mistakes. This paper uses the manifold learning algorithm for fault pattern clustering recognition, and through experiments on the algorithm is evaluated.

An adaptive Dendrite-HDMR metamodeling technique for high dimensional problems

2022 ◽  
pp. 1-38
Author(s):  
Qi Zhang ◽  
Yizhong Wu ◽  
Li Lu ◽  
Ping Qiao
Keyword(s):  
Adaptive Sampling ◽  
Clustering Algorithm ◽  
Learning Algorithm ◽  
Weight Ratio ◽  
Propulsion System ◽  
High Dimensional ◽  
High Dimensional Model Representation ◽  
K Nearest Neighbor ◽  
Metamodeling Technique ◽  
Explicit Expressions

Abstract High dimensional model representation (HDMR), decomposing the high-dimensional problem into summands of different order component terms, has been widely researched to work out the dilemma of “curse-of-dimensionality” when using surrogate techniques to approximate high-dimensional problems in engineering design. However, the available one-metamodel-based HDMRs usually encounter the predicament of prediction uncertainty, while current multi-metamodels-based HDMRs cannot provide simple explicit expressions for black-box problems, and have high computational complexity in terms of constructing the model by the explored points and predicting the responses of unobserved locations. Therefore, aimed at such problems, a new stand-alone HDMR metamodeling technique, termed as Dendrite-HDMR, is proposed in this study based on the hierarchical Cut-HDMR and the white-box machine learning algorithm, Dendrite Net. The proposed Dendrite-HDMR not only provides succinct and explicit expressions in the form of Taylor expansion, but also has relatively higher accuracy and stronger stability for most mathematical functions than other classical HDMRs with the assistance of the proposed adaptive sampling strategy, named KKMC, in which k-means clustering algorithm, k-Nearest Neighbor classification algorithm and the maximum curvature information of the provided expression are utilized to sample new points to refine the model. Finally, the Dendrite-HDMR technique is applied to solve the design optimization problem of the solid launch vehicle propulsion system with the purpose of improving the impulse-weight ratio, which represents the design level of the propulsion system.

Rank-based risk target data analysis using digital twin on oil pipeline network based on manifold learning

2022 ◽  
pp. 095440892110732
Author(s):  
E.B. Priyanka ◽  
S. Thangavel ◽  
Priyanka Prabhakaran
Keyword(s):  
Data Analysis ◽  
Manifold Learning ◽  
Clustering Algorithm ◽  
Oil And Gas ◽  
Pipeline System ◽  
Security Risk ◽  
Oil Pipeline ◽  
Cloud Server ◽  
Risk Rate ◽  
Risk Predictor

Oil and Gas Pipeline (OGP) projects face a wide scope of wellbeing and security Risk Factors (RFs) all around the world, especially in the oil and gas delivering nations having influencing climate and unsampled data. Lacking data about the reasons for pipeline risk predictor and unstructured data about the security of the OGP prevent endeavors of moderating such dangers. This paper, subsequently, means to foster a risk analyzing framework in view of a comprehensive methodology of recognizing, dissecting and positioning the related RFs, and assessing the conceivable pipeline characteristics. Hazard Mitigation Methods (HMMs), which are the initial steps of this approach. A new methodology has been created to direct disappointment investigation of pinhole erosion in pipelines utilizing the typical pipeline risk strategy and erosion climate reenactments during a full life pattern of the pipeline. Hence in the proposed work, manifold learning with rank based clustering algorithm is incorporated with the cloud server for improved data analysis. The probability risk rate is identified from the burst pressure by clustering the normal and leak category to improve the accuracy of the prediction system experimented on the lab-scale oil pipeline system. The numerical results like auto-correlation, periodogram, Laplace transformed P-P Plot are utilized to estimate the datasets restructured by the manifold learning approach. The obtained experimental results shows that the cloud server datasets are clustered with rank prioritization to make proactive decision in faster manner by distinguishing labelled and unlabeled pressure attributes.

Self-Organizing Map Learning Nonlinearly Embedded Manifolds

2005 ◽  
Vol 4 (1) ◽  
pp. 22-31 ◽  
Author(s):  
Timo Similä
Keyword(s):  
Learning Algorithm ◽  
Image Data ◽  
High Dimensional ◽  
Locally Linear Embedding ◽  
Complex Data ◽  
Dimensional Manifold ◽  
Self Organizing Map ◽  
Training Strategy ◽  
Low Dimensional ◽  
Self Organizing

One of the main tasks in exploratory data analysis is to create an appropriate representation for complex data. In this paper, the problem of creating a representation for observations lying on a low-dimensional manifold embedded in high-dimensional coordinates is considered. We propose a modification of the Self-organizing map (SOM) algorithm that is able to learn the manifold structure in the high-dimensional observation coordinates. Any manifold learning algorithm may be incorporated to the proposed training strategy to guide the map onto the manifold surface instead of becoming trapped in local minima. In this paper, the Locally linear embedding algorithm is adopted. We use the proposed method successfully on several data sets with manifold geometry including an illustrative example of a surface as well as image data. We also show with other experiments that the advantage of the method over the basic SOM is restricted to this specific type of data.

scholarly journals Distilling nanoscale heterogeneity of amorphous silicon using tip-enhanced Raman spectroscopy (TERS) via multiresolution manifold learning

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Guang Yang ◽  
Xin Li ◽  
Yongqiang Cheng ◽  
Mingchao Wang ◽  
Dong Ma ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Amorphous Silicon ◽  
Manifold Learning ◽  
Domain Knowledge ◽  
Amorphous Materials ◽  
Learning Algorithm ◽  
Structural Heterogeneity ◽  
Dimensional Manifold ◽  
Large Area ◽  
Tip Enhanced Raman Spectroscopy

AbstractAccurately identifying the local structural heterogeneity of complex, disordered amorphous materials such as amorphous silicon is crucial for accelerating technology development. However, short-range atomic ordering quantification and nanoscale spatial resolution over a large area on a-Si have remained major challenges and practically unexplored. We resolve phonon vibrational modes of a-Si at a lateral resolution of <60 nm by tip-enhanced Raman spectroscopy. To project the high dimensional TERS imaging to a two-dimensional manifold space and categorize amorphous silicon structure, we developed a multiresolution manifold learning algorithm. It allows for quantifying average Si-Si distortion angle and the strain free energy at nanoscale without a human-specified physical threshold. The multiresolution feature of the multiresolution manifold learning allows for distilling local defects of ultra-low abundance (< 0.3%), presenting a new Raman mode at finer resolution grids. This work promises a general paradigm of resolving nanoscale structural heterogeneity and updating domain knowledge for highly disordered materials.

scholarly journals Opinion Texts Clustering Using Manifold Learning Based on Sentiment and Semantics Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Sajjad Jahanbakhsh Gudakahriz ◽  
Amir Masoud Eftekhari Moghadam ◽  
Fariborz Mahmoudi
Keyword(s):  
Social Networks ◽  
Manifold Learning ◽  
Clustering Algorithm ◽  
High Dimensional ◽  
Text Representation ◽  
Effective Solution ◽  
High Dimensions ◽  
The Third ◽  
Nonlinear Dimension Reduction ◽  
Nonlinear Dimension

Nowadays, opinion texts are quickly published on websites and social networks by various users in the form of short texts and also in high volumes and various fields. Because these texts reflect the opinions of many users, their processing and analysis, such as clustering, can be very useful in a variety of applications including politics, industry, commerce, and economics. High dimensions of the text representation decrease efficiency of clustering, and an effective solution for this challenge is reducing dimensions of texts. Manifold learning is a powerful tool for nonlinear dimension reduction of high-dimensional data. Therefore, in this paper, for increasing efficiency of opinion texts clustering, by manifold learning, dimensions of the represented opinion texts are reduced based on sentiment and semantics, and their intrinsic dimensions are extracted. Then, the clustering algorithm is applied to dimension-reduced opinion texts. The proposed approach helps us to cluster opinion texts with simultaneous consideration of sentiment and semantics, which has received very little attention in the previous works. This type of clustering helps users of opinion texts to obtain more useful information from texts and also provides more accurate summaries in applications, such as the summarization of opinion texts. Experimental results on three datasets show better performance of the proposed approach on opinion texts in terms of important measures for evaluating clustering efficiency. An improvement of about 9% is observed in terms of accuracy on the third dataset and clustering based on sentiment and semantics.

A BOOSTED MANIFOLD LEARNING FOR AUTOMATIC FACE RECOGNITION

2010 ◽  
Vol 24 (02) ◽  
pp. 321-335 ◽  
Author(s):  
CHUNYUAN LU ◽  
JIANMIN JIANG ◽  
GUOCAN FENG
Keyword(s):  
Face Recognition ◽  
Manifold Learning ◽  
Reduction Method ◽  
Learning Algorithm ◽  
High Dimensional ◽  
Nonlinear Structures ◽  
Effective Dimension ◽  
Dimension Reduction Method ◽  
Effective Dimension Reduction ◽  
Analysis Computer

Manifold learning is an effective dimension reduction method to extract nonlinear structures from high dimensional data. Recently, manifold learning has received much attention within the research communities of image analysis, computer vision and document data analysis. In this paper, we propose a boosted manifold learning algorithm towards automatic 2D face recognition by using AdaBoost to select the best possible discriminating projection for manifold learning to exploit the strength of both techniques. Experimental results support that the proposed algorithm improves over existing benchmarks in terms of stability and recognition precision rates.

FSLLE: A Fast K Selection Algorithm for Locally Linear Embedding

2018 ◽  
Vol 17 (01) ◽  
pp. 1850003
Author(s):  
Jin-Hang Liu ◽  
Tao Peng ◽  
Xiaogang Zhao ◽  
Kunfang Song ◽  
Minghua Jiang ◽  
...  
Keyword(s):  
Face Recognition ◽  
Spatial Correlation ◽  
Manifold Learning ◽  
High Dimensional ◽  
Locally Linear Embedding ◽  
Dimensional Manifold ◽  
Correlation Index ◽  
Recognition Algorithms ◽  
Linear Embedding ◽  
Locally Linear

Data in a high-dimensional data space may reside in a low-dimensional manifold embedded within the high-dimensional space. Manifold learning discovers intrinsic manifold data structures to facilitate dimensionality reductions. We propose a novel manifold learning technique called fast [Formula: see text] selection for locally linear embedding or FSLLE, which judiciously chooses an appropriate number (i.e., parameter [Formula: see text]) of neighboring points where the local geometric properties are maintained by the locally linear embedding (LLE) criterion. To measure the spatial distribution of a group of neighboring points, FSLLE relies on relative variance and mean difference to form a spatial correlation index characterizing the neighbors’ data distribution. The goal of FSLLE is to quickly identify the optimal value of parameter [Formula: see text], which aims at minimizing the spatial correlation index. FSLLE optimizes parameter [Formula: see text] by making use of the spatial correlation index to discover intrinsic structures of a data point’s neighbors. After implementing FSLLE, we conduct extensive experiments to validate the correctness and evaluate the performance of FSLLE. Our experimental results show that FSLLE outperforms the existing solutions (i.e., LLE and ISOMAP) in manifold learning and dimension reduction. We apply FSLLE to face recognition in which FSLLE achieves higher accuracy than the state-of-the-art face recognition algorithms. FSLLE is superior to the face recognition algorithms, because FSLLE makes a good tradeoff between classification precision and performance.

Optimal Classification of Hypersonic Inlet Start/Unstart Based on Manifold Learning

2013 ◽  
Vol 274 ◽  
pp. 200-203
Author(s):  
Ri Sheng Zheng ◽  
Jun Tao Chang ◽  
Hui Xin He ◽  
Fu Chen
Keyword(s):  
Manifold Learning ◽  
Learning Algorithm ◽  
Operation Mode ◽  
High Dimensional ◽  
Classification Criterion ◽  
Decision Tree Algorithm ◽  
Time Data ◽  
Hypersonic Inlet ◽  
Optimal Classification

Inlet start/unstart detection has been the focus of researching hypersonic inlet, the operation mode of the inlet detection is the prerequisite for the unstart protection control of scramjet. Actually, due to computational complexity and high dimension discrete experimental data, all of these factors are against for the classification of real-time data. To solve this problem, firstly, the 2-D wind tunnel experiment is carried out, inlet start/unstart experiment phenomenon are analyzed; Secondly, isomap algorithm is introduced to reduce high dimensional data , the optimal classification method were obtained with the weighted embedded manifold learning algorithm, At last the superiority of the classification criterion is verified by decision tree algorithm.

scholarly journals The Value of Manifold Learning Algorithms in Simplifying Complex Datasets for More Efficacious Analysis

2020 ◽  
pp. 13-20
Author(s):  
Muhammad Amjad
Keyword(s):  
Data Analysis ◽  
Manifold Learning ◽  
Dimensional Space ◽  
Feature Space ◽  
Topological Data Analysis ◽  
High Dimensional ◽  
Dimensional Manifold ◽  
Low Dimensional ◽  
Low Dimensional Manifold ◽  
Complex Datasets

Advances in manifold learning have proven to be of great benefit in reducing the dimensionality of large complex datasets. Elements in an intricate dataset will typically belong in high-dimensional space as the number of individual features or independent variables will be extensive. However, these elements can be integrated into a low-dimensional manifold with well-defined parameters. By constructing a low-dimensional manifold and embedding it into high-dimensional feature space, the dataset can be simplified for easier interpretation. In spite of this elemental dimensionality reduction, the dataset’s constituents do not lose any information, but rather filter it with the hopes of elucidating the appropriate knowledge. This paper will explore the importance of this method of data analysis, its applications, and its extensions into topological data analysis.

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