• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.4814-4834
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• 2018

This paper presents an architecture for wireless sensor networks (WSNs) with blind source separation (BSS) applied to retrieve the received mixing signals of the sink nodes first. The little-to-no need of prior knowledge about the source signals of the sink nodes in the BSS method is obviously advantageous for WSNs. The optimization problem of the BSS of multiple independent source signals with complex and noncircular distributions from observed sensor nodes is considered and addressed. This paper applies Castella's reference-based scheme to Novey's negentropy-based algorithms, and then proposes a novel fast fixed-point (FastICA) algorithm, defined as the reference-signal negentropy complex FastICA (RSNT-cFastICA) for complex-valued noncircular-distribution source signals. The proposed method for the sink nodes is substantially more efficient than Novey's quasi-Newton algorithm in terms of computational speed under large numbers of samples, can effectively improve the power consumption effeciency of the sink nodes, and is significantly beneficial for WSNs and wireless communication networks (WCNs). The effectiveness and performance of the proposed method are validated and compared with three related BSS algorithms through theoretical analysis and simulations.

• 전자공학회논문지 IE
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• v.45 no.3
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• pp.20-26
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• 2008

Two independent component analysis(ICA) algorithms were applied for the recognition of speech signals corrupted by a car engine noise. Speech recognition was performed by hidden markov model(HMM) for the estimated signals and recognition rates were compared with those of orginal speech signals which are not corrupted. Two different ICA methods were applied for the estimation of speech signals, one of which is FastICA algorithm that maximizes negentropy, the other is information-maximization approach that maximizes the mutual information between inputs and outputs to give maximum independence among outputs. Word recognition rate for the Korean news sentences spoken by a male anchor is 87.85%, while there is 1.65% drop of performance on the average for the estimated speech signals by FastICA and 2.02% by information-maximization for the various signal to noise ratio(SNR). There is little difference between the methods.

• Proceedings of the Korea Multimedia Society Conference
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• 2002.05c
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• pp.325-329
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• 2002

본 연구에서는 할선법과 모멘트를 조합한 학습알고리즘의 신경망 기반 독립성분분석 기법을 제안하였다. 제안된 알고리즘은 할선법과 모멘트에 기초를 둔 고정점 알고리즘의 독립성분분석 기법이다. 여기서 할선법은 독립성분 상호간의 정보를 최소화하기 위해 negentropy를 최대화는 과정에서 요구되는 1차 미분에 따른 계산량을 줄이기 위함이고, 모멘트는 최대화 과정에서 발생하는 발진을 억제하여 보다 빠른 학습을 위함이다. 제안된 기법을 256×256 픽셀의 8개 지문영상에서 임의 혼합행렬에 따라 발생되는 혼합지문들을 각각 대상으로 시뮬레이션한 결과, 할선법만에 기초한 기법보다 우수한 분리성능과 빠른 학습속도가 있음을 확인하였다.

• The Korean Journal of Applied Statistics
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• v.30 no.6
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• pp.889-899
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• 2017

Independent component analysis is a multivariate approach to separate mixed signals into original signals. It is the most widely used method of blind source separation technique. ICA uses linear transformations such as principal component analysis and factor analysis, but differs in that ICA requires statistical independence and non-Gaussian assumptions of original signals. PCA have a natural ordering based on cumulative proportion of explained variance; howerver, ICA algorithms cannot identify the unique optimal ordering of the components. It is meaningful to set order because major components can be used for further analysis such as clustering and low-dimensional graphs. In this paper, we compare the performance of several criteria to determine the order of the components. Kurtosis, absolute value of kurtosis, negentropy, Kolmogorov-Smirnov statistic and sum of squared coefficients are considered. The criteria are evaluated by their ability to classify known groups. Two types of data are analyzed for illustration.

• International Journal of Control, Automation, and Systems
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• v.4 no.6
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• pp.705-713
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• 2006

For the analysis of multiunit extracellular neural signals as multiple spike trains, neural spike sorting is essential. Existing algorithms for the spike sorting have been unsatisfactory when the signal-to-noise ratio(SNR) is low, especially for implementation of fully-automated systems. We present a novel method that shows satisfactory performance even under low SNR, and compare its performance with a recent method based on principal component analysis(PCA) and fuzzy c-means(FCM) clustering algorithm. Our system consists of a spike detector that shows high performance under low SNR, a feature extractor that utilizes projection pursuit based on negentropy maximization, and an unsupervised classifier based on Gaussian mixture model. It is shown that the proposed feature extractor gives better performance compared to the PCA, and the proposed combination of spike detector, feature extraction, and unsupervised classification yields much better performance than the PCA-FCM, in that the realization of fully-automated unsupervised spike sorting becomes more feasible.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.04b
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• pp.1037-1040
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• 2002

본 연구에서는 효율적인 학습알고리즘을 가지는 신경망 기반 독립성분분석 기법을 제안하였다. 제안된 기법은 할선법에 기초를 둔 fixed point 알고리즘의 신경망 기반 독립성분분석 기법이다. 여기서 할선법은 독립성분 상호간의 정보를 최소화하기 위해 negentropy를 최대화는 과정에서 요구되는 1차 미분에 따른 계산량을 줄이기 위함이다. 제안된 기법을 500개의 데이터를 가지는 4개 신호들로부터 임의의 혼합 행렬에 따라 발생되는 혼합신호들을 각각 대상으로 시뮬레이션 한 결과, 우수한 분리성능과 빠른 학습 속도가 있음을 확인하였다.

• Environmental Analysis Health and Toxicology
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• v.14 no.1_2
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• pp.45-54
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• 1999

Quantitative strucrure-activity relationships between the toxicity (LD$\_$50/) and molecular properties of amine and nitro compounds were tested. The all 19 compounds showed low correlations below 0.500 to their LD$\_$50/ values. When amine or nitro compounds were taken separately, the correlation between the calculated chemphysico parameters and LD$\_$50/ were also poor (r$^2$=0.4911, 3967 repectively). The overall relationships among the QSAR parameters were investigated. Molecular weight shows a high correlation with total surface area (r$^2$=0.9287); 0.9090 for zero-order connectivity and second-order connectivity : 0.8784 for bioconcentration factor and second-order connectivity. When amine compounds were taken to perform the statistical treatment, the relationships between parameters were as follows: 0.8436 for volume-negentropy; 0.8925 for volume-bioconcentration factor; 0.9929 for zero-order connectivity-Kow; zero-order connectivity-bioconcentration factor; 0.9141 for zero-order connectivity-solubility; 0.9718 for solubility-bioconcentration factor; 0.9894 for solubility-bioconcentration factor and 0.9319 for Kow-bioconcentration factor. On the other hand, nitro compounds showed different relationships as follows: 0.8952 for volume-I/O character; 0.9520 for volume-total surface area: 0.9351 for volume-molecular weight; 0.9351 for volume-MW; 0.9961 for Kow-Koc; 0.8455 for Kow-bioconcentration factor; 0.8879 for Koc-bioconcentration factor; 0.9987 for MW-total surface area respectively.

• Journal of the Society of Naval Architects of Korea
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• v.49 no.6
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• pp.498-503
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• 2012

Independent component analysis (ICA) technique based on statistical independency of the signals is known as suitable to identify the source signals by measuring and separating mixed signals through transfer paths and has successfully applied in the field of medical care, communications and so forth. In this study, the ICA technique is introduced for the identification of excitation sources from measured vibration signals of structures, which can be done by evaluating negentropy of centered and whitened vibration signals and correlation of separated signals. To validate the method, numerical analyses are carried out for a plate and a cylinder structure. The results show that the method can be applied efficiently to source identification of complex structures. Nevertheless, additional studies would be required to complement problems of occasional inaccuracy.

• International Journal of Aeronautical and Space Sciences
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• v.13 no.1
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• pp.14-26
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• 2012

The only extraterrestrial life detection experiments ever conducted were the three which were components of the 1976 Viking Mission to Mars. Of these, only the Labeled Release experiment obtained a clearly positive response. In this experiment $^{14}C$ radiolabeled nutrient was added to the Mars soil samples. Active soils exhibited rapid, substantial gas release. The gas was probably $CO_2$ and, possibly, other radiocarbon-containing gases. We have applied complexity analysis to the Viking LR data. Measures of mathematical complexity permit deep analysis of data structure along continua including signal vs. noise, entropy vs.negentropy, periodicity vs. aperiodicity, order vs. disorder etc. We have employed seven complexity variables, all derived from LR data, to show that Viking LR active responses can be distinguished from controls via cluster analysis and other multivariate techniques. Furthermore, Martian LR active response data cluster with known biological time series while the control data cluster with purely physical measures. We conclude that the complexity pattern seen in active experiments strongly suggests biology while the different pattern in the control responses is more likely to be non-biological. Control responses that exhibit relatively low initial order rapidly devolve into near-random noise, while the active experiments exhibit higher initial order which decays only slowly. This suggests a robust biological response. These analyses support the interpretation that the Viking LR experiment did detect extant microbial life on Mars.