• Title/Summary/Keyword: network component analysis

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Oil Flow Distribution Control of Engine Lubrication System Using Orifice Component (오리피스를 이용한 엔진 윤활시스템 유량분배 제어)

  • Yun Jeong-Eui
    • Tribology and Lubricants
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    • v.22 no.1
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    • pp.47-52
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    • 2006
  • It is very important to control pressure and flow rate distribution on each component of engine lubrication network. Sometimes many kinds of orifice are used to control flow rate in the hydraulic lubrication field. In this study orifices were adopted on the lubrication network to control oil flow rate distribution. And unsteady transient flow network analysis was carried out to find out the effects of orifices on the engine oil circuit system.

Analysis of Molecular Pathways in Pancreatic Ductal Adenocarcinomas with a Bioinformatics Approach

  • Wang, Yan;Li, Yan
    • Asian Pacific Journal of Cancer Prevention
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    • v.16 no.6
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    • pp.2561-2567
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    • 2015
  • Pancreatic ductal adenocarcinoma (PDAC) is a leading cause of cancer death worldwide. Our study aimed to reveal molecular mechanisms. Microarray data of GSE15471 (including 39 matching pairs of pancreatic tumor tissues and patient-matched normal tissues) was downloaded from Gene Expression Omnibus (GEO) database. We identified differentially expressed genes (DEGs) in PDAC tissues compared with normal tissues by limma package in R language. Then GO and KEGG pathway enrichment analyses were conducted with online DAVID. In addition, principal component analysis was performed and a protein-protein interaction network was constructed to study relationships between the DEGs through database STRING. A total of 532 DEGs were identified in the 38 PDAC tissues compared with 33 normal tissues. The results of principal component analysis of the top 20 DEGs could differentiate the PDAC tissues from normal tissues directly. In the PPI network, 8 of the 20 DEGs were all key genes of the collagen family. Additionally, FN1 (fibronectin 1) was also a hub node in the network. The genes of the collagen family as well as FN1 were significantly enriched in complement and coagulation cascades, ECM-receptor interaction and focal adhesion pathways. Our results suggest that genes of collagen family and FN1 may play an important role in PDAC progression. Meanwhile, these DEGs and enriched pathways, such as complement and coagulation cascades, ECM-receptor interaction and focal adhesion may be important molecular mechanisms involved in the development and progression of PDAC.

Fault Diagnosis Method of Complex System by Hierarchical Structure Approach (계층구조 접근에 의한 복합시스템 고장진단 기법)

  • Bae, Yong-Hwan;Lee, Seok-Hee
    • Journal of the Korean Society for Precision Engineering
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    • v.14 no.11
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    • pp.135-146
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    • 1997
  • This paper describes fault diagnosis method in complex system with hierachical structure similar to human body structure. Complex system is divided into unit, item and component. For diagnosing this hierarchical complex system, it is necessary to implement special neural network. Fault diagnosis system can forecast faults in a system and decide from current machine state signal information. Comparing with other diagnosis system for single fault, the developed system deals with multiple fault diagnosis comprising Hierarchical Neural Network(HNN). HNN consists of four level neural network, first level for item fault symptom classification, second level for item fault diagnosis, third level for component symptom classification, forth level for component fault diagnosis. UNIX IPC(Inter Process Communication) is used for implementing HNN wiht multitasking and message transfer between processes in SUN workstation with X-Windows(Motif). We tested HNN at four units, seven items per unit, seven components per item in a complex system. Each one neural newtork operate as a separate process in HNN. The message queue take charge of information exdhange and cooperation between each neural network.

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Principal Component Analysis of BGP Update Streams

  • Xu, Kuai;Chandrashekar, Jaideep;Zhang, Zhi-Li
    • Journal of Communications and Networks
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    • v.12 no.2
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    • pp.191-197
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    • 2010
  • In this paper, we propose a novel methodology to identify border gateway protocol (BGP) updates associated with major events - affecting network reachability to multiple ASes - and separate them (statistically) from those attributable to minor events, which individually generate few updates, but collectively form the persistent background noise observed at BGP vantage points. Our methodology is based on principal component analysis, which enables us to transform and reduce the BGP updates into different AS clusters that are likely affected by distinct major events. We demonstrate the accuracy and effectiveness of our methodology through simulations and real BGP data.

Performance of music section detection in broadcast drama contents using independent component analysis and deep neural networks (ICA와 DNN을 이용한 방송 드라마 콘텐츠에서 음악구간 검출 성능)

  • Heo, Woon-Haeng;Jang, Byeong-Yong;Jo, Hyeon-Ho;Kim, Jung-Hyun;Kwon, Oh-Wook
    • Phonetics and Speech Sciences
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    • v.10 no.3
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    • pp.19-29
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    • 2018
  • We propose to use independent component analysis (ICA) and deep neural network (DNN) to detect music sections in broadcast drama contents. Drama contents mainly comprise silence, noise, speech, music, and mixed (speech+music) sections. The silence section is detected by signal activity detection. To detect the music section, we train noise, speech, music, and mixed models with DNN. In computer experiments, we used the MUSAN corpus for training the acoustic model, and conducted an experiment using 3 hours' worth of Korean drama contents. As the mixed section includes music signals, it was regarded as a music section. The segmentation error rate (SER) of music section detection was observed to be 19.0%. In addition, when stereo mixed signals were separated into music signals using ICA, the SER was reduced to 11.8%.

Neural Learning Algorithms for Independent Component Analysis

  • Choi, Seung-Jin
    • Journal of IKEEE
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    • v.2 no.1 s.2
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    • pp.24-33
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    • 1998
  • Independent Component analysis (ICA) is a new statistical method for extracting statistically independent components from their linear instantaneous mixtures which are generated by an unknown linear generative model. The recognition model is learned in unsupervised manner so that the recovered signals by the recognition model become the possibly scaled estimates of original source signals. This paper addresses the neural learning approach to ICA. As recognition models a linear feedforward network and a linear feedback network are considered. Associated learning algorithms for both networks are derived from maximum likelihood and information-theoretic approaches, using natural Riemannian gradient [1]. Theoretical results are confirmed by extensive computer simulations.

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Reliability Analysis system For Network with Dependent Components (종속을 고려한 Network동적 신뢰도 분석 시스템)

  • Yun, Won-Young;Ha, Young-Ju
    • IE interfaces
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    • v.10 no.2
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    • pp.41-50
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    • 1997
  • This paper considers the reliability computation of the network with dependent components and a software system is developed for supporting the reliability analysis and improvement of the system reliability. At first, We propose the common cause failure and load sharing models as the typical models considering the dynamic behaviors of networks with dependent components. Secondly, the algorithm is proposed to obtain the network reliability and reliability importance of component. The software, Delphi, is used to develop the our reliability analysis system.

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A Study on the Design of Sensor Fault Detection System Using AANN(AutoAssociative Neural Network) (AANN 기법을 이용한 온-라인 센서 고장 검출 알고리즘 개발에 관한 연구)

  • Han, Yun-Jong;Bae, Sang-Wook;Kim, Sung-Ho
    • Proceedings of the KIEE Conference
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    • 2002.07d
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    • pp.2268-2271
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    • 2002
  • NLPCA(Nonlinear principal component analysis is a novel technique for multivariate data analysis, similar to the weil-known method of principal component analysis. NLPCA operates by a feedforward neural network called AANN(AutoAssociative Neural Network) which performs the identity mapping. In this work, a sensor fault defection system based on NLPCA is presented. To verify its applicability, simulation study on the data supplied from Saemangeum measurement stations is executed.

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Projection spectral analysis: A unified approach to PCA and ICA with incremental learning

  • Kang, Hoon;Lee, Hyun Su
    • ETRI Journal
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    • v.40 no.5
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    • pp.634-642
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    • 2018
  • Projection spectral analysis is investigated and refined in this paper, in order to unify principal component analysis and independent component analysis. Singular value decomposition and spectral theorems are applied to nonsymmetric correlation or covariance matrices with multiplicities or singularities, where projections and nilpotents are obtained. Therefore, the suggested approach not only utilizes a sum-product of orthogonal projection operators and real distinct eigenvalues for squared singular values, but also reduces the dimension of correlation or covariance if there are multiple zero eigenvalues. Moreover, incremental learning strategies of projection spectral analysis are also suggested to improve the performance.

Artificial Intelligence Engine for Numerical Analysis of Surface Waves (표면파의 수치해석을 위한 인공지능 엔진 개발)

  • Kwak Hyo-Gyoung;Kim Jae-Hong
    • Proceedings of the Computational Structural Engineering Institute Conference
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    • 2006.04a
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    • pp.89-96
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    • 2006
  • Nondestructive evaluation using surface waves needs an analytical solution for the reference value to compare with experimental data. Finite element analysis is very powerful tool to simulate the wave propagation, but has some defects. It is very expensive and high time-complexity for the required high resolution. For those reasons, it is hard to implement an optimization problem in the actual situation. The developed engine in this paper can substitute for the finite element analysis of surface waves propagation, and it accomplishes the fast analysis possible to be used in optimization. Including this artificial intelligence engine, most of soft computing algorithms can be applied on the special database. The database of surface waves propagation is easily constructed with the results of finite element analysis after reducing the dimensions of data. The principal wavelet-component analysis is an efficient method to simplify the transient wave signal into some representative peaks. At the end, artificial neural network based on the database make it possible to invent the artificial intelligence engine.

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