JournalTOCs

International Journal of Machine Intelligence and Sensory Signal Processing
Number of Followers: 3

Hybrid journal (It can contain Open Access articles)
ISSN (Print) 2048-9161 - ISSN (Online) 2048-917X
Published by Inderscience Publishers

[439 journals]

Optimal selecting and scaling the earthquake accelerograms according to
Eurocode 8 using ranked particles optimisation
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  Authors: Amirfarzad Behnam, Amir Nasrollahi
  Pages: 97 - 115
  Abstract: In the present study, we aimed at selecting and scaling accelerograms using metaheuristic algorithms. Selecting and scaling accelerograms is an important step in nonlinear structural analysis. Usually, the accelerograms are selected from the same soil type and they are multiplied to one scaling factor to move its response spectrum above the standard design spectra. In this research, we modified the previous formulation and used ranked particles optimisation (RPO) to optimise the process of selecting and scaling. The records were selected from the database including 380 earthquake records from Pacific Earthquake Engineering Research Center (PEER). This database contains most of strong earthquakes occurred all over the world. For each soil type of Eurocode 8, accelerograms were selected from the same soil type and from the whole database. The results show that regardless of soil types, when the accelerograms are selected from the whole database, the cost function has smaller value. The same methodology can be easily used for other standards by slight modification of the cost and the penalty functions.
  Keywords: accelerograms; scaling; ranked particles optimisation; RPO; Eurocode 8; response spectra; design spectra
  Citation: International Journal of Machine Intelligence and Sensory Signal Processing, Vol. 2, No. 2 (2018) pp. 97 - 115
  PubDate: 2018-07-03T23:20:50-05:00
  DOI: 10.1504/IJMISSP.2018.092929
  Issue No: Vol. 2, No. 2 (2018)
Optimal selection of learning parameters for regularised random vector
functional-link networks-based soft measuring model
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  Authors: Amirfarzad Behnam, Amir Nasrollahi
  Pages: 116 - 134
  Abstract: Random vector functional-link networks (RVFLNs) with one single hidden layer structure have been used widely for soft measuring model construction. In which, the input weights and biases are produced randomly and the output weights are computed analytically by a Moore-Penrose generalised inverse method. Regularised RVFLN (RRVFLN) can prevent over-fitting problem and reduce complexity of the constructed model by using the ridge regression method. Several learning parameters, such as range of random input weights and bias, number of hidden nodes and regularising factor are data dependent. This paper aims to develop a composite differential evolution (CoDE)-based optimal selection method to address the three learning parameters of RRVFLN. Experiments on some benchmark datasets are carried out to validate the proposed method.
  Keywords: random parameter scope; composite differential evolution; CoDE; random vector functional-link networks; RVFLNs; learning parameters optimal selection
  Citation: International Journal of Machine Intelligence and Sensory Signal Processing, Vol. 2, No. 2 (2018) pp. 116 - 134
  PubDate: 2018-07-03T23:20:50-05:00
  DOI: 10.1504/IJMISSP.2018.092930
  Issue No: Vol. 2, No. 2 (2018)
Time-aware efficient prediction and anomaly detection for
large-scale light curves
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  Authors: Jing Bi, Tianzhi Feng, Haitao Yuan, Zhen Wei
  Pages: 135 - 150
  Abstract: In the era of data explosion, how to process large-scale data is one of the most important problems. This work focuses on the processing of large-scale astronomical data. In the field of astronomy, stellar brightness is an important attribute of the stars. Ground-based wide-angle camera array (GWAC) can provide a huge volume of data for the brightness analysis of numerous stars. Based on the GWAC data, this work aims to analyse and predict the light curves, as well as to conduct early detection of the abnormal variation in brightness of stars for the special astronomical phenomena. To reduce the data processing time, this work proposes a parallel auto-regressive integrated moving average (PARIMA) model to process the mini-GWAC data. After determining the parameters, the model is used to predict the abnormal phenomena. Furthermore, the simulation experiment shows that the proposed PARIMA method can accurately predict and alarm in time.
  Keywords: PARIMA; real-time analysis; big data processing; large-scale light curves; anomaly detection; time-aware prediction; time series analysis; machine learning; astronomical phenomena; multi-process mechanism; machine intelligence; sensory signal pr
  Citation: International Journal of Machine Intelligence and Sensory Signal Processing, Vol. 2, No. 2 (2018) pp. 135 - 150
  PubDate: 2018-07-03T23:20:50-05:00
  DOI: 10.1504/IJMISSP.2018.092935
  Issue No: Vol. 2, No. 2 (2018)
Review of techniques for predicting hard drive failure with SMART
attributes
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  Authors: Marco Garcia, Vladimir Ivanov, Anastasia Kozar, Stanislav Litvinov, Alexey Reznik, Vitaly Romanov, Giancarlo Succi
  Pages: 151 - 164
  Abstract: Hard drive failure prediction is still a relevant problem today. A number of statistical and machine learning techniques were proposed to improve failure forecasting accuracy after SMART was introduced. SMART is a diagnostics tool that aims at providing forehand failure warnings. Failure prediction methods can be viewed as a part of reliability analysis - the field that was studied intensively for decades. However, in some situations available techniques cannot be applied due to a simple reason - information at hand is not always sufficient for reliable prediction. SMART's goal is to provide meaningful information that can signify problems with the health condition of a hard drive and failure prediction techniques can leverage this data to provide timely and reliable warnings. To find the best failure forecasting algorithm and evaluate the possibility of its widespread deployment, we review existing datasets with SMART attributes, methods for feature selection for hard drive failure prediction.
  Keywords: reliability; failure modelling; cyberphysical systems; machine intelligence
  Citation: International Journal of Machine Intelligence and Sensory Signal Processing, Vol. 2, No. 2 (2018) pp. 151 - 164
  PubDate: 2018-07-03T23:20:50-05:00
  DOI: 10.1504/IJMISSP.2018.092936
  Issue No: Vol. 2, No. 2 (2018)
Multi-view data ensemble clustering: a cluster-level
perspective
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  Authors: Jiye Liang, Qianyu Shi, Xingwang Zhao
  Pages: 165 - 188
  Abstract: Ensemble clustering has recently emerged a powerful clustering analysis technology for multi-view data. From the existing work, these techniques held great promise, but most of them are inefficient for large data. Some researchers have also proposed efficient ensemble clustering algorithms, but these algorithms devote to data objects with the same feature spaces, which are not satisfied for multi-view data. To overcome these deficiencies, an efficient ensemble clustering algorithm for multi-view mixed data is developed from the cluster-level perspective. Firstly, a set of clustering solutions are produced with the K-prototypes clustering algorithm on each view multiple times, respectively. Then, a cluster-cluster similarity matrix is constructed by considering all the clustering solutions. Next, the METIS algorithm is conduct meta-clustering based on the similarity matrix. After that, the final clustering results are obtained by applying majority voting to assign the objects to their corresponding clusters based on the meta-clustering. The corresponding time complexity of the proposed algorithm is analysed as well. Experimental results on several multi-view datasets demonstrated the superiority of our proposed algorithm.
  Keywords: multi-view data; mixed data; k-prototypes clustering algorithm; ensemble clustering
  Citation: International Journal of Machine Intelligence and Sensory Signal Processing, Vol. 2, No. 2 (2018) pp. 165 - 188
  PubDate: 2018-07-03T23:20:50-05:00
  DOI: 10.1504/IJMISSP.2018.092938
  Issue No: Vol. 2, No. 2 (2018)