Machine Performance Degradation Recognition Using Locality Preserving Projections and Clustering Approach

2012 ◽  
Vol 443-444 ◽  
pp. 929-934
Author(s):  
Jian Bo Yu ◽  
Jian Ping Liu ◽  
Mei Fang Liu ◽  
Ji Ting Yin ◽  
Yong Guo Wang
Keyword(s):  
Principal Component ◽  
Performance Degradation ◽  
Experimental Result ◽  
Test Bed ◽  
Machine Performance ◽  
Locality Preserving ◽  
Systematic Feature ◽  
Automatic Guidance ◽  
Clustering Approach ◽  
Low Dimensional

The sensitivity of various features that are characteristics of machine performance may vary significantly under different working conditions. Thus it is critical to devise a systematic feature extraction (FE) approach that provides a useful and automatic guidance on using the most effective features for machine performance recognition without human intervention. This paper proposes a locality preserving projection (LPP)-based FE approach for machine performance degradation recognition. Different from principal component analysis (PCA) that aims to discover the global structure of the Euclidean space, LPP is capable to discover local structure of the data manifold. This may enable LPP to find more meaningful low-dimensional information hidden in the high-dimensional observations compared with PCA. This experimental result on a bearing test-bed shows that LPP-based FE improves the performance of recognizers for identifying performance degradation of bearings.

scholarly journals Unsupervised Identification of Targeted Spectra Applying Rank1-NMF and FCC Algorithms in Long-Wave Hyperspectral Infrared Imagery

2021 ◽  
Vol 13 (11) ◽  
pp. 2125
Author(s):  
Bardia Yousefi ◽  
Clemente Ibarra-Castanedo ◽  
Martin Chamberland ◽  
Xavier P. V. Maldague ◽  
Georges Beaudoin
Keyword(s):  
Matched Filter ◽  
Principal Component ◽  
Hyperspectral Data ◽  
Clustering Methods ◽  
Spectral Angle Mapper ◽  
Long Wave ◽  
Clustering Approach ◽  
Spectral Comparison ◽  
Computational Simplicity ◽  
Mineral Identification

Clustering methods unequivocally show considerable influence on many recent algorithms and play an important role in hyperspectral data analysis. Here, we challenge the clustering for mineral identification using two different strategies in hyperspectral long wave infrared (LWIR, 7.7–11.8 μm). For that, we compare two algorithms to perform the mineral identification in a unique dataset. The first algorithm uses spectral comparison techniques for all the pixel-spectra and creates RGB false color composites (FCC). Then, a color based clustering is used to group the regions (called FCC-clustering). The second algorithm clusters all the pixel-spectra to directly group the spectra. Then, the first rank of non-negative matrix factorization (NMF) extracts the representative of each cluster and compares results with the spectral library of JPL/NASA. These techniques give the comparison values as features which convert into RGB-FCC as the results (called clustering rank1-NMF). We applied K-means as clustering approach, which can be modified in any other similar clustering approach. The results of the clustering-rank1-NMF algorithm indicate significant computational efficiency (more than 20 times faster than the previous approach) and promising performance for mineral identification having up to 75.8% and 84.8% average accuracies for FCC-clustering and clustering-rank1 NMF algorithms (using spectral angle mapper (SAM)), respectively. Furthermore, several spectral comparison techniques are used also such as adaptive matched subspace detector (AMSD), orthogonal subspace projection (OSP) algorithm, principal component analysis (PCA), local matched filter (PLMF), SAM, and normalized cross correlation (NCC) for both algorithms and most of them show a similar range in accuracy. However, SAM and NCC are preferred due to their computational simplicity. Our algorithms strive to identify eleven different mineral grains (biotite, diopside, epidote, goethite, kyanite, scheelite, smithsonite, tourmaline, pyrope, olivine, and quartz).

scholarly journals The Highly Uniform Photoresponsivity from Visible to Near IR Light in Sb2Te3 Flakes

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1535
Author(s):  
Shiu-Ming Huang ◽  
Jai-Lung Hung ◽  
Mitch Chou ◽  
Chi-Yang Chen ◽  
Fang-Chen Liu ◽  
...  
Keyword(s):  
Band Structure ◽  
Energy Difference ◽  
Experimental Result ◽  
Light Illumination ◽  
Near Ir ◽  
State Band ◽  
Valence Bands ◽  
Low Dimensional ◽  
Low Dimensional Materials ◽  
Ir Light

Broadband photosensors have been widely studied in various kinds of materials. Experimental results have revealed strong wavelength-dependent photoresponses in all previous reports. This limits the potential application of broadband photosensors. Therefore, finding a wavelength-insensitive photosensor is imperative in this application. Photocurrent measurements were performed in Sb2Te3 flakes at various wavelengths ranging from visible to near IR light. The measured photocurrent change was insensitive to wavelengths from 300 to 1000 nm. The observed wavelength response deviation was lower than that in all previous reports. Our results show that the corresponding energies of these photocurrent peaks are consistent with the energy difference of the density of state peaks between conduction and valence bands. This suggests that the observed photocurrent originates from these band structure peak transitions under light illumination. Contrary to the most common explanation that observed broadband photocurrent carrier is mainly from the surface state in low-dimensional materials, our experimental result suggests that bulk state band structure is the main source of the observed photocurrent and dominates the broadband photocurrent.

scholarly journals Numerical Valuation of American Basket Options via Partial Differential Complementarity Problems

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1498
Author(s):  
Karel J. in’t Hout ◽  
Jacob Snoeijer
Keyword(s):  
Principal Component Analysis ◽  
Numerical Experiments ◽  
Complementarity Problems ◽  
Principal Component ◽  
Partial Differential ◽  
Basket Options ◽  
Basket Option ◽  
Option Values ◽  
Convergence Behaviour ◽  
Low Dimensional

We study the principal component analysis based approach introduced by Reisinger and Wittum (2007) and the comonotonic approach considered by Hanbali and Linders (2019) for the approximation of American basket option values via multidimensional partial differential complementarity problems (PDCPs). Both approximation approaches require the solution of just a limited number of low-dimensional PDCPs. It is demonstrated by ample numerical experiments that they define approximations that lie close to each other. Next, an efficient discretisation of the pertinent PDCPs is presented that leads to a favourable convergence behaviour.

scholarly journals Optimizing the structure and movement of a robotic bat with biological kinematic synergies

2018 ◽  
Vol 37 (10) ◽  
pp. 1233-1252 ◽  
Author(s):  
Jonathan Hoff ◽  
Alireza Ramezani ◽  
Soon-Jo Chung ◽  
Seth Hutchinson
Keyword(s):  
Principal Component Analysis ◽  
Topological Structure ◽  
Degrees Of Freedom ◽  
Dimensional Space ◽  
Principal Component ◽  
Biologically Inspired ◽  
Wing Kinematics ◽  
Wing Motion ◽  
Kinematic Synergies ◽  
Low Dimensional

In this article, we present methods to optimize the design and flight characteristics of a biologically inspired bat-like robot. In previous, work we have designed the topological structure for the wing kinematics of this robot; here we present methods to optimize the geometry of this structure, and to compute actuator trajectories such that its wingbeat pattern closely matches biological counterparts. Our approach is motivated by recent studies on biological bat flight that have shown that the salient aspects of wing motion can be accurately represented in a low-dimensional space. Although bats have over 40 degrees of freedom (DoFs), our robot possesses several biologically meaningful morphing specializations. We use principal component analysis (PCA) to characterize the two most dominant modes of biological bat flight kinematics, and we optimize our robot’s parametric kinematics to mimic these. The method yields a robot that is reduced from five degrees of actuation (DoAs) to just three, and that actively folds its wings within a wingbeat period. As a result of mimicking synergies, the robot produces an average net lift improvesment of 89% over the same robot when its wings cannot fold.

scholarly journals Small-variance asymptotics for non-parametric online robot learning

2018 ◽  
Vol 38 (1) ◽  
pp. 3-22 ◽  
Author(s):  
Ajay Kumar Tanwani ◽  
Sylvain Calinon
Keyword(s):  
Mixture Models ◽  
Dirichlet Process ◽  
Large Scale ◽  
Principal Component ◽  
Small Variance ◽  
Remote Manipulation ◽  
State Duration ◽  
Duration Information ◽  
Low Dimensional ◽  
Non Parametric

Small-variance asymptotics is emerging as a useful technique for inference in large-scale Bayesian non-parametric mixture models. This paper analyzes the online learning of robot manipulation tasks with Bayesian non-parametric mixture models under small-variance asymptotics. The analysis yields a scalable online sequence clustering (SOSC) algorithm that is non-parametric in the number of clusters and the subspace dimension of each cluster. SOSC groups the new datapoint in low-dimensional subspaces by online inference in a non-parametric mixture of probabilistic principal component analyzers (MPPCA) based on a Dirichlet process, and captures the state transition and state duration information online in a hidden semi-Markov model (HSMM) based on a hierarchical Dirichlet process. A task-parameterized formulation of our approach autonomously adapts the model to changing environmental situations during manipulation. We apply the algorithm in a teleoperation setting to recognize the intention of the operator and remotely adjust the movement of the robot using the learned model. The generative model is used to synthesize both time-independent and time-dependent behaviors by relying on the principles of shared and autonomous control. Experiments with the Baxter robot yield parsimonious clusters that adapt online with new demonstrations and assist the operator in performing remote manipulation tasks.

Novel method for bearing performance degradation assessment – A kernel locality preserving projection-based approach

2013 ◽  
Vol 228 (3) ◽  
pp. 548-560 ◽  
Author(s):  
Chuang Sun ◽  
Zhousuo Zhang ◽  
Zhengjia He ◽  
Zhongjie Shen ◽  
Binqiang Chen ◽  
...  
Keyword(s):  
Test Data ◽  
Performance Degradation ◽  
Inner Product ◽  
Data Sets ◽  
Degradation Index ◽  
Locality Preserving Projection ◽  
Locality Preserving ◽  
Non Linear ◽  
Linear Information ◽  
Novel Method

Bearing performance degradation assessment is meaningful for keeping mechanical reliability and safety. For this purpose, a novel method based on kernel locality preserving projection is proposed in this article. Kernel locality preserving projection extends the traditional locality preserving projection into the non-linear form by using a kernel function and it is more appropriate to explore the non-linear information hidden in the data sets. Considering this point, the kernel locality preserving projection is used to generate a non-linear subspace from the normal bearing data. The test data are then projected onto the subspace to obtain an index for assessing bearing degradation degrees. The degradation index that is expressed in the form of inner product indicates similarity of the normal data and the test data. Validations by using monitoring data from two experiments show the effectiveness of the proposed method.

scholarly journals A Geometric Framework for Understanding Dynamic Information Integration in Context-Dependent Computation

2021 ◽  
Author(s):  
Xiaohan Zhang ◽  
Shenquan Liu ◽  
Zhe Sage Chen
Keyword(s):  
Decision Making ◽  
Prefrontal Cortex ◽  
Information Integration ◽  
Principal Component ◽  
Population Coding ◽  
Geometric Framework ◽  
Context Dependent ◽  
Activity State ◽  
Low Dimensional ◽  
Context Cues

AbstractPrefrontal cortex plays a prominent role in performing flexible cognitive functions and working memory, yet the underlying computational principle remains poorly understood. Here we trained a rate-based recurrent neural network (RNN) to explore how the context rules are encoded, maintained across seconds-long mnemonic delay, and subsequently used in a context-dependent decision-making task. The trained networks emerged key experimentally observed features in the prefrontal cortex (PFC) of rodent and monkey experiments, such as mixed-selectivity, sparse representations, neuronal sequential activity and rotation dynamics. To uncover the high-dimensional neural dynamical system, we further proposed a geometric framework to quantify and visualize population coding and sensory integration in a temporally-defined manner. We employed dynamic epoch-wise principal component analysis (PCA) to define multiple task-specific subspaces and task-related axes, and computed the angles between task-related axes and these subspaces. In low-dimensional neural representations, the trained RNN first encoded the context cues in a cue-specific subspace, and then maintained the cue information with a stable low-activity state persisting during the delay epoch, and further formed line attractors for sensor integration through low-dimensional neural trajectories to guide decision making. We demonstrated via intensive computer simulations that the geometric manifolds encoding the context information were robust to varying degrees of weight perturbation in both space and time. Overall, our analysis framework provides clear geometric interpretations and quantification of information coding, maintenance and integration, yielding new insight into the computational mechanisms of context-dependent computation.

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