• Title/Summary/Keyword: State clustering

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Development of Diagnostic Expert System for Rotating Machinery with Journal Bearing-Research on the Diagnosis of the Nonlinear Characteristics of Rotor System (저어널 베어링으로 지지된 회전축의 이상상태 진단을 위한 진단 전문가 시스템의 개발-로타시스템의 비선형 특성 진단을 위한 연구)

  • 유송민;김영진;박상신
    • Tribology and Lubricants
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    • v.17 no.2
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    • pp.153-161
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    • 2001
  • The development of techniques in diagnosing the state of the system is one of the essential tools in establishing the automation and unmanned manufacturing system for the realization of CIM/FMS in the fields. In this paper, we developed various diagnostic schemes for the journal bearing supported rotor system. Up to now, vibration of the shaft, measurement of the displacement and the temperature have been used for diagnostic tools, however, the statistical features only could not differentiate the state from states. Thus, we identified the sensor data f3r the steady state in the signal processing and then applied the fuzzy c-mean technology to cope with the nonlinear characteristics of the system. This will, in return, establish a possible diagnostic system for the rotor system in the fields.

Iterative LBG Clustering for SIMO Channel Identification

  • Daneshgaran, Fred;Laddomada, Massimiliano
    • Journal of Communications and Networks
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    • v.5 no.2
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    • pp.157-166
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    • 2003
  • This paper deals with the problem of channel identification for Single Input Multiple Output (SIMO) slow fading channels using clustering algorithms. Due to the intrinsic memory of the discrete-time model of the channel, over short observation periods, the received data vectors of the SIMO model are spread in clusters because of the AWGN noise. Each cluster is practically centered around the ideal channel output labels without noise and the noisy received vectors are distributed according to a multivariate Gaussian distribution. Starting from the Markov SIMO channel model, simultaneous maximum ikelihood estimation of the input vector and the channel coefficients reduce to one of obtaining the values of this pair that minimizes the sum of the Euclidean norms between the received and the estimated output vectors. Viterbi algorithm can be used for this purpose provided the trellis diagram of the Markov model can be labeled with the noiseless channel outputs. The problem of identification of the ideal channel outputs, which is the focus of this paper, is then equivalent to designing a Vector Quantizer (VQ) from a training set corresponding to the observed noisy channel outputs. The Linde-Buzo-Gray (LBG)-type clustering algorithms [1] could be used to obtain the noiseless channel output labels from the noisy received vectors. One problem with the use of such algorithms for blind time-varying channel identification is the codebook initialization. This paper looks at two critical issues with regards to the use of VQ for channel identification. The first has to deal with the applicability of this technique in general; we present theoretical results for the conditions under which the technique may be applicable. The second aims at overcoming the codebook initialization problem by proposing a novel approach which attempts to make the first phase of the channel estimation faster than the classical codebook initialization methods. Sample simulation results are provided confirming the effectiveness of the proposed initialization technique.

Lossless Compression for Hyperspectral Images based on Adaptive Band Selection and Adaptive Predictor Selection

  • Zhu, Fuquan;Wang, Huajun;Yang, Liping;Li, Changguo;Wang, Sen
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.14 no.8
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    • pp.3295-3311
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    • 2020
  • With the wide application of hyperspectral images, it becomes more and more important to compress hyperspectral images. Conventional recursive least squares (CRLS) algorithm has great potentiality in lossless compression for hyperspectral images. The prediction accuracy of CRLS is closely related to the correlations between the reference bands and the current band, and the similarity between pixels in prediction context. According to this characteristic, we present an improved CRLS with adaptive band selection and adaptive predictor selection (CRLS-ABS-APS). Firstly, a spectral vector correlation coefficient-based k-means clustering algorithm is employed to generate clustering map. Afterwards, an adaptive band selection strategy based on inter-spectral correlation coefficient is adopted to select the reference bands for each band. Then, an adaptive predictor selection strategy based on clustering map is adopted to select the optimal CRLS predictor for each pixel. In addition, a double snake scan mode is used to further improve the similarity of prediction context, and a recursive average estimation method is used to accelerate the local average calculation. Finally, the prediction residuals are entropy encoded by arithmetic encoder. Experiments on the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) 2006 data set show that the CRLS-ABS-APS achieves average bit rates of 3.28 bpp, 5.55 bpp and 2.39 bpp on the three subsets, respectively. The results indicate that the CRLS-ABS-APS effectively improves the compression effect with lower computation complexity, and outperforms to the current state-of-the-art methods.

Similarity Analysis of Hospitalization using Crowding Distance

  • Jung, Yong Gyu;Choi, Young Jin;Cha, Byeong Heon
    • International journal of advanced smart convergence
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    • v.5 no.2
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    • pp.53-58
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    • 2016
  • With the growing use of big data and data mining, it serves to understand how such techniques can be used to understand various relationships in the healthcare field. This study uses hierarchical methods of data analysis to explore similarities in hospitalization across several New York state counties. The study utilized methods of measuring crowding distance of data for age-specific hospitalization period. Crowding distance is defined as the longest distance, or least similarity, between urban cities. It is expected that the city of Clinton have the greatest distance, while Albany the other cities are closer because they are connected by the shortest distance to each step. Similarities were stronger across hospital stays categorized by age. Hierarchical clustering can be applied to predict the similarity of data across the 10 cities of hospitalization with the measurement of crowding distance. In order to enhance the performance of hierarchical clustering, comparison can be made across congestion distance when crowding distance is applied first through the application of converting text to an attribute vector. Measurements of similarity between two objects are dependent on the measurement method used in clustering but is distinguished from the similarity of the distance; where the smaller the distance value the more similar two things are to one other. By applying this specific technique, it is found that the distance between crowding is reduced consistently in relationship to similarity between the data increases to enhance the performance of the experiments through the application of special techniques. Furthermore, through the similarity by city hospitalization period, when the construction of hospital wards in cities, by referring to results of experiments, or predict possible will land to the extent of the size of the hospital facilities hospital stay is expected to be useful in efficiently managing the patient in a similar area.

A study in fault detection and diagnosis of induction motor by clustering and fuzzy fault tree (클러스터링과 fuzzy fault tree를 이용한 유도전동기 고장 검출과 진단에 관한 연구)

  • Lee, Seong-Hwan;Shin, Hyeon-Ik;Kang, Sin-Jun;Woo, Cheon-Hui;Woo, Gwang-Bang
    • Journal of Institute of Control, Robotics and Systems
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    • v.4 no.1
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    • pp.123-133
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    • 1998
  • In this paper, an algorithm of fault detection and diagnosis during operation of induction motors under the condition of various loads and rates is investigated. For this purpose, the spectrum pattern of input currents is used in monitoring the state of induction motors, and by clustering the spectrum pattern of input currents, the newly occurrence of spectrum patterns caused by faults are detected. For the diagnosis of the fault detected, a fuzzy fault tree is designed, and the fuzzy relation equation representing the relation between an induction motor fault and each fault type, is solved. The solution of the fuzzy relation equation shows the possibility of occurence of each fault. The results obtained are summarized as follows : (1) Using clustering algorithm by unsupervised learning, an on-line fault detection method unaffected by the characteristics of loads and rates is implemented, and the degree of dependency for experts during fault detection is reduced. (2) With the fuzzy fault tree, the fault diagnosis process become systematic and expandable to the whole system, and the diagnosis for sub-systems can be made as an object-oriented module.

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Routing and Forwarding with Flexible Addressing

  • Poutievski, Leonid B.;Calvert, Kenneth L.;Griffioen, James N.
    • Journal of Communications and Networks
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    • v.9 no.4
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    • pp.383-393
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    • 2007
  • We present a new network-layer architecture that provides generalized addressing. The forwarding infrastructure is independent of the addressing architecture, so multiple addressing architectures can be used simultaneously. We compare our solution with the existing Internet protocols for unicast and multicast services, given the address assignment used in the Internet. By means of an extensive simulation study, we determine the range of parameters for which the overhead costs(delay, state, and network load) of our service are comparable to those of the Internet.

Electronic Structure and Magnetic Moments of Copper-atom in/on GaN Semiconductor

  • Kang, Byung-Sub;Lee, Haeng-Ki
    • Journal of Magnetics
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    • v.15 no.2
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    • pp.51-55
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    • 2010
  • The electronic and magnetic properties of Cu-doped GaN with a Cu concentration of 6.25% and 12.5% are examined theoretically using the full-potential linear muffin-tin orbital method. The magnetic moment of Cu atoms decreases with increasing Cu concentration. The spin-polarization of Cu atoms is reduced due to the Cu d-d interaction depending on the distance between the nearest neighbouring Cu atoms. Cu atoms exhibits a clustering tendency in GaN. For Cu-adsorbed GaN thin films with a surface coverage of 0.25, the ferromagnetic state is found to be the energetically favourable state with an induced magnetic moment of $0.54\;{\mu}_B$ per supercell.

Developments in Hull Strength Monitoring (Developments in Hull Strength Monitoring)

  • P. A. Thomson;Ph. D BMT SeaTech Ltd.
    • Journal of the Korean Society of Marine Environment & Safety
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    • v.2 no.1
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    • pp.143-143
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    • 1996
  • Recent Class requirements and IMO recommendations concerning Hull Strength Monitoring (HSM) have prompted an increasing number of shipowner to adopt monitoring systems on bulk carriers and tanker. Such systems are designed to give warning when stress levels and the frequency and magnitude of ship motions approach levels which require corrective action. When fitted these systems provide enhanced operational safety and efficiency. This paper describes a development beyond the standard BMT HSM system through the integration of stress, motion and radar-based sea state monitoring with powerful, on-board, artificial intelligence (AI) tools. The latter utilises conceptual clustering techniques as an aid to pattern recognition in stress, fatigue. motion and sea state data clusters. This, in turn, provides additional operational guidance for ship's staff. Feedback from applications of the standard BMT HSM and extended HSM systems on board the British Steel Bulk Shipping fleet is described.

Source Environment Feature Related Phylogenetic Distribution Pattern of Anoxygenic Photosynthetic Bacteria as Revealed by pufM Analysis

  • Zeng, Yonghui;Jiao, Nianzhi
    • Journal of Microbiology
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    • v.45 no.3
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    • pp.205-212
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    • 2007
  • Anoxygenic photosynthesis, performed primarily by anoxygenic photosynthetic bacteria (APB), has been supposed to arise on Earth more than 3 billion years ago. The long established APB are distributed in almost every corner where light can reach. However, the relationship between APB phylogeny and source environments has been largely unexplored. Here we retrieved the pufM sequences and related source information of 89 pufM containing species from the public database. Phylogenetic analysis revealed that horizontal gene transfer (HGT) most likely occurred within 11 out of a total 21 pufM subgroups, not only among species within the same class but also among species of different phyla or subphyla. A clear source environment feature related phylogenetic distribution pattern was observed, with all species from oxic habitats and those from anoxic habitats clustering into independent subgroups, respectively. HGT among ancient APB and subsequent long term evolution and adaptation to separated niches may have contributed to the coupling of environment and pufM phylogeny.

AUTOMATIC TUNING OF FUZZY OPTIMAL CONTROL SYSTEM

  • Hoon-Kang;Lee, Hong-Gi-;Kim, Yong-Ho-;Jeon, Hong-Tae
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1993.06a
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    • pp.1195-1198
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    • 1993
  • We investigate a systematic design procedure of automated rule generation of fuzzy logic based controller for uncertain dynamic systems such as an engine dynamic model.“Automated Tuning”means autonomous clustering or collection of such meaningful transitional relations in the state-space. Optimal control strategies are included in the design procedures, such as minimum squared error, minimum time, minimum energy or combined performance criteria. Fuzzy feedback control systems designed by the cell-state transition method have the properties of closed-loop stability, robustness under parameter variabtions, and a certain degree of optimality. Most of all, the main advantage of the proposed approach is that reliability can be potentially increased even if a large grain of uncertainty is involved within the control system under consideration. A numerical example is shown in which we apply our strategic fuzzy controller design to a highly nonlinear model of engine idle speed contr l.

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