• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.1
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• pp.22-28
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• 2021

Multivariate processes, such as chemical and mechanical process, power plants are operated in a state where several facilities are complexly connected, the fault of a particular system can also have fatal consequences for the entire process. In addition, since process data is measured in an unstable environment, outlier is likely to be include in the data. Therefore, monitoring technology is essential, which can remove outlier from measured data and detect failures in advance. In this paper, data obtained from dynamic and multivariate process models was used to detect fault in various type of processes. The dynamic process is a simulation of a process with autoregressive property, and the multivariate process is a model that describes a situation when a specific sensor fault. Mahalanobis distance was used to remove outlier contained in the data generated by dynamic process model and multivariate process model, and fault detection was performed using ICA. For comparison, we compared performance with and a conventional single ICA method. The proposed fault detection method improves performance by 0.84%p for bias data and 6.82%p for drift data in the dynamic process. In the case of the multivariate process, the performance was improves by 3.78%p, therefore, the proposed method showed better fault detection performance.

• Journal of Korea Multimedia Society
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• v.3 no.3
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• pp.253-262
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• 2000

This paper proposes a new RBF neural network that determines the number and the center of hidden neurons based on the adaptive feature extraction for the input data. The principal component analysis is applied for extracting adaptively the features by reducing the dimension of the given input data. It can simultaneously achieve a superior property of both the principal component analysis by mapping input data into set of statistically independent features and the RBF neural networks. The proposed neural networks has been applied to classify the 200 breast cancer databases by 2-class. The simulation results shows that the proposed neural networks has better performances of the learning time and the classification for test data, in comparison with those using the k-means clustering algorithm. And it is affected less than the k-means clustering algorithm by the initial weight setting and the scope of the smoothing factor.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.1C
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• pp.65-71
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• 2004

We improved the MLLR speaker adaptation algorithm with reduction of the order of HMM parameters using PCA(Principle Component Analysis) or ICA(Independent Component Analysis). To find a smaller set of variables with less redundancy, we adapt PCA(principal component analysis) and ICA(independent component analysis) that would give as good a representation as possible, minimize the correlations between data elements, and remove the axis with less covariance or higher-order statistical independencies. Ordinary MLLR algorithm needs more than 30 seconds adaptation data to represent higher word recognition rate of SD(Speaker Dependent) models than of SI(Speaker Independent) models, whereas proposed algorithm needs just more than 10 seconds adaptation data. 10 components for ICA and PCA represent similar performance with 36 components for ordinary MLLR framework. So, compared with ordinary MLLR algorithm, the amount of total computation requested in speaker adaptation is reduced by about 1/167 in proposed MLLR algorithm.

• Korean Journal of Food Science and Technology
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• v.32 no.1
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• pp.132-139
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• 2000

This study was conducted to optimize the emulsion process and the spray drying process for the microencapsulation of flavor compounds. Using the wall system selected, emulsion process for microencapsulation was optimized on the change of the pressure of piston-type homogenizer. Emulsification pressure of 34.5 MPa was found to be the most suitable for preparing flavor emulsion. Effects of drying temperature and atomizer speed of the spray drier on total oil, surface oil, and flavor release of the flavor powder were investigated using response surface methodology. The optimum spray drying conditions for minimal surface oil and flavor release and maximum total oil were $170{\circ}C$ inlet temperature and 15,000 rpm atomizer speed. The spray-dried powder processed with the highest drying temperature showed spherically-shaped particles with smooth surface.

• The Journal of the Korea Contents Association
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• v.9 no.4
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• pp.121-130
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• 2009

In this paper, feature extraction methods, which is one field of reducing dimensions of high-dimensional data, are empirically investigated. We selected the traditional PCA(Principal Component Analysis), ICA(Independent Component Analysis), NMF(Non-negative Matrix Factorization), and sNMF(Sparse NMF) for comparisons. ICA has a similar feature with the simple cell of V1. NMF implemented a "parts-based representation in the brain" and sNMF is a improved version of NMF. In order to visually investigate the extracted features, handwritten digits are handled. Also, the extracted features are used to train multi-layer perceptrons for recognition test. The characteristic of each feature extraction method will be useful when applying feature extraction methods to many real-world problems.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.9
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• pp.838-842
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• 2007

A brain-computer interface(BCI) system is a communication channel which transforms a subject's thought process into command signals to control various devices. These systems use electroencephalographic signals or the neuronal activity of many single neurons. The presented study deals with an efficient analysis method of neuronal signals from a BCI System using an independent component analysis(ICA) algorithm. The BCI system was implemented to generate event signals coding movement information of the subject. To apply the ICA algorithm, we obtained the perievent histograms of neuronal signals recorded from prefrontal cortex(PFC) region during target-to-goal(TG) task trials in the BCI system. The neuronal signals were then smoothed over 5ms intervals by low-pass filtering. The matrix of smoothed signals was then rearranged such that each signal was represented as a column and each bin as a row. Each column was also normalized to have a unit variance. As a result, we verified that different patterns of the neuronal signals are dependent on the target position and predefined event signals.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.4
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• pp.366-370
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• 2013

Regression Analysis is an analyzing method of regression model to explain the statistical relationship between explanatory variable and response variables. This paper introduce Theil's method to find a fuzzy regression model which explain the relationship between explanatory variable and response variables. Theil's method is a robust method which is not sensive to outliers. Theil's method use medians of rate of increment based on randomly chosen pairs of each components of ${\alpha}$-level sets of fuzzy data in order to estimate the coefficients of fuzzy regression model. We propose an example to show Theil's estimator is robust than the Least squares estimator.

• Journal of The Korean Society of Agricultural Engineers
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• v.51 no.2
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• pp.1-6
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• 2009

Acetogen과 같은 일부 혐기성미생물은 소위 acetyl-CoA 경로에 의해 아세트산, 에탄올, 그리고 몇 가지 생화학 물질을 생산한다. 이 경로에서는 일산화탄소를 기질로 이용할 수 있다. 일산화탄소 이외에 수소가 이용될 수 있다. 즉 이들 미생물은 독립영양생물로서 이산화탄소와 태양광에너지를 이용하는 녹색식물과 비유될 수 있으며, 일산화탄소는 탄소원으로서 동시에 에너지원으로서 이용된다. 본 연구에서는 혐기성 소화액 중 아세트산을 생성하는 미생물이 존재한다고 가정하고, 일산화탄소와 수소가 주 가연성분인 합성가스를 공급하면 추가의 메탄이 생성가능성을 평가하였다. 혐기성 소화과정에서 발생되는 메탄은 주로 아세트산으로부터 만들어지므로 일산화탄소를 공급하는 경우 추가로 메탄이 생성될 것으로 추측할 수 있기 때문이다. 이를 확인하기 위하여 현재 운영중인 바이오가스 생산 설비로부터 얻은 혐기성 소화액을 생물반응조에 넣은 후, 합성가스를 순환-공급하여 가스 생산량의 변화 및 조성을 분석하였다. 질소가스를 공급한 대조구와는 달리 일산화탄소 또는 합성가스를 공급한 경우에는 메탄가스가 생산되는 것을 확인하였다. 질소가스를 공급한 대조구와는 달리 일산화탄소 또는 합성가스를 공급한 경우에는 메탄가스가 생산되는 것을 확인하였다. 일산화탄소만을 공급했을 때에는 이산화탄소의 생성으로 가스 생산량이 증가하였으나, 수소가 포함된 합성가스를 공급하였을 때에는 이산화탄소가 탄소원이로 소비되어 가스 저장도 내의 가스량이 감소하는 것을 확인할 수 있었다. 가스화공정에 으해 얻어지는 합성가스는 온도와 가스 조성을 고러할 때, 바이오가스 생산을 위한 혐기성 소화조와 연계하면 소화조의 가온에 필요한 열을 공급할 수 있고 바이오가스 중 이산화탄소 농도를 낮추어 발열량을 개선할 수 있을 것으로 판단된다.

• Journal of Korea Water Resources Association
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• v.31 no.3
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• pp.317-326
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• 1998

Water consumption characteristics on the northern part of Seoul were analyzed using wavelet transform with a base function of Coiflets 5. It turns out that long term evolution mode detected at 212 scale in 1995 was in a shape of hyperbolic tangent over the entire period due to the development of Sanggae resident site. Furthermore, there was seasonal water demand having something to do with economic cycle which reached its peak at the ends of June and December. The amount of this additional consumption was about $1,700\;\textrm{cm}^3/hr$ on June and $500\;\textrm{cm}^3/hr$ on December. It was also shown that the periods of energy containing sinusoidal component were 3.13 day, 33.33 hr, 23.98 hr and 12 hr, respectively, and the amplitude of 23.98 hr component was the most humongous. The components of relatively short frequency detected at $2^i$[i = 1,2,…12] scale were following Gaussian PDF. The most reliable predictive models are multiple AR[32,16,23] and ARMA[20, 16, 10, 23] which the input of temperature from the view point of minimized predictive error, mutual independence or residuals and the availableness of reliable meteorological data. The predicted values of water supply were quite consistent with the measured data which cast a possibility of the deployment of the predictive model developed in this study for the optimal management of water supply facilities.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.2 s.314
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• pp.10-17
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• 2007

The echo signal on ultrasonic transducer is a mixed signal from tissues, blood vessel walls, blood cells and noise. In this mixed-signal, the signal reflected from tissues and blood vessel walls is called clutter. It is necessary to extract pure blood signal from this mixed-signal, when measuring blood flow velocity with medical ultrasonic system The quality of measured blood flow velocity is highly dependent on sufficient attenuation of the clutter signals. In this paper, we suggest a clutter rejection method using ICA For simulation, the echo signals are generated by Field n ultrasonic simulation program In this echo signals, independent signals are separated by using ICA Then the blood signal is obtained from the separated signals. Blood flow velocity is measured by 2D autocorrelation method. We compare ICA clutter rejection method with PCA-based eigen filter method using both measured blood flow velocity profiles by 2D autocorrelation. In simulation results, ICA clutter rejection method can be better applied measuring blood flow velocity in noisy echo signals.