• Journal of Biomedical Engineering Research
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• v.20 no.4
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• pp.435-442
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• 1999

A fuzzy-ART(adaptive resonance theory) network for the PVC(premature ventricular contraction) classification using wavelet coefficient is designed. This network consists of the feature extraction and learning of the fuzzy-ART network. In the first step, we have detected the QRS from the ECG signal in order to set the threshold range for feature extraction and the detected QRS was divided into several frequency bands by wavelet transformation using Haar wavelet. Among the low-frequency bands, only the 6th coefficient(D6) are selected as the input feature. After that, the fuzzy-ART network for classification of the PVC is learned by using input feature which comprises of binary data converted by applying threshold to D6. The MIT/BIH database including the PVC is used for the evaluation. The designed fuzzy-ART network showed the PVC classification ratio of 96.52%.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.6
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• pp.504-511
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• 2013

This paper proposes improved detection schemes for concealed micro-electronic devices using clustering and classification of radio frequency harmonics in order to protect intellectual property rights. In general, if a radio wave with a specific fundamental frequency is propagated from the transmitter of a classifier to a concealed object, the second and the third harmonics will be returned as the radio wave is reflected. Using this principle, we exploit the fuzzy c-means clustering and the ${\kappa}$-nearest neighbor classification for detecting diverse concealed objects. Simulation results indicate that the proposed scheme can detect electronic devices and metal devices in various learning environments by efficient classification. Thus, the proposed schemes can be utilized as an effective detection method for concealed micro-electronic device to protect intellectual property rights.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.4
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• pp.42-53
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• 2012

In pattern recognition and machine learning society, classification has been a classical problem and the most widely researched area. Adaptive boosting also known as Adaboost has been successfully applied to binary classification problems. It is a kind of boosting algorithm capable of constructing a strong classifier through a weighted combination of weak classifiers. On the other hand, the PCA and LDA algorithms are the most popular linear feature extraction methods used mainly for dimensionality reduction. In this paper, the combination of Adaboost and feature extraction methods is proposed for efficient classification of two class data. Conventionally, in classification problems, the roles of feature extraction and classification have been distinct, i.e., a feature extraction method and a classifier are applied sequentially to classify input variable into several categories. In this paper, these two steps are combined into one resulting in a good classification performance. More specifically, each projection vector is treated as a weak classifier in Adaboost algorithm to constitute a strong classifier for binary classification problems. The proposed algorithm is applied to UCI dataset and FRGC dataset and showed better recognition rates than sequential application of feature extraction and classification methods.

• Journal of Ecology and Environment
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• v.38 no.2
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• pp.133-143
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• 2015

Freshwater macrophytes improve the structural heterogeneity of microhabitats in water, often providing an important habitat for zooplankton. Some studies have focused on the overall influence of macrophytes on zooplankton, but the effects of macrophyte in relation to different habitat characteristics of zooplankton (e.g., epiphytic and pelagic) have not been intensively studied. We hypothesized that different habitat structures (i.e., macrophyte habitat) would strongly affect zooplankton distribution. We investigated zooplankton density and diversity, macrophyte characteristics (dry weight and species number), and environmental parameters in 40 shallow wetlands in South Korea. Patterns in the data were analyzed using a self-organizing map (SOM), which extracts information through competitive and adaptive properties. A total of 20 variables (11 environmental parameters and 9 zooplankton groups) were patterned onto the SOM. Based on a U-matrix, 3 clusters were identified from the model. Zooplankton assemblages were positively related to macrophyte characteristics (i.e., dry weight and species number). In particular, epiphytic species (i.e., epiphytic rotifers and cladocerans) exhibited a clear relationship with macrophyte characteristics, while large biomass and greater numbers of macrophyte species supported high zooplankton assemblages. Consequently, habitat heterogeneity in the macrophyte bed was recognized as an important factor to determine zooplankton distribution, particularly in epiphytic species. The results indicate that macrophytes are critical for heterogeneity in lentic freshwater ecosystems, and the inclusion of diverse plant species in wetland construction or restoration schemes is expected to generate ecologically healthy food webs.

• Journal of Korea Multimedia Society
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• v.13 no.1
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• pp.143-152
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• 2010

Because of a rising in the standard of living and the development of medical technology, Korea is expected to become an aging society more than 14% elderly population and the silver generation will be responsible for the large portion of economic activities. The silver generation has relatively diminished in perception, learning, and exercise due to the physical aging. Therefore, it is important the development of game contents which can constantly provide the maintenance function of physical and mental health and the functional exercise with spending the leisure time. In this paper, we suggest a design method for developing the game contents which are able to help the silver generation to strengthen their lower extremities and to enjoy their leisure time. The proposed method will enable to lead the silver generation to carry on the physical activities with the amusement of playing a game through the functional design concepts appropriate for the ability of the silver generation's perception and physical activities, an adaptive game process customized by the individual capability and a user-friendly sensory interface.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.5
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• pp.44-54
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• 2010

Optimal efficiency control of synchronous reluctance motor(SynRM) is very important in the sense of energy saving and conservation of natural environment because the efficiency of the SynRM is generally lower than that of other types of AC motors. This paper is proposed a novel efficiency optimization control of SynRM considering iron loss using multi adaptive fuzzy learning controller(AFLC). The optimal current ratio between torque current and exciting current is analytically derived to drive SynRM at maximum efficiency. This paper is proposed an efficiency optimization control for the SynRM which minimizes the copper and iron losses. There exists a variety of combinations of d and q-axis current which provide a specific motor torque. The objective of the efficiency optimization control is to seek a combination of d and q-axis current components, which provides minimum losses at a certain operating point in steady state. The control performance of the proposed controller is evaluated by analysis for various operating conditions. Analysis results are presented to show the validity of the proposed algorithm.

• Journal of Intelligence and Information Systems
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• v.4 no.1
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• pp.133-147
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• 1998

Recently, many researchers have been involved in finding deterministic equations which can accurately predict future event, based on chaotic theory, or fractal theory. The theory says that some events which seem very random but internally deterministic can be accurately predicted by fractal equations. In contrast to the conventional methods, such as AR model, MA, model, or ARIMA model, the fractal equation attempts to discover a deterministic order inherent in time series data set. In discovering deterministic order, researchers have found that neural networks are much more effective than the conventional statistical models. Even though prediction accuracy of the network can be different depending on the topological structure and modification of the algorithms, many researchers asserted that the neural network systems outperforms other systems, because of non-linear behaviour of the network models, mechanisms of massive parallel processing, generalization capability based on adaptive learning. However, recent survey shows that prediction accuracy of the forecasting models can be determined by the model structure and data structures. In the experiments based on actual economic data sets, it was found that the prediction accuracy of the neural network model is similar to the performance level of the conventional forecasting model. Especially, for the data set which is deterministically chaotic, the AR model, a conventional statistical model, was not significantly different from the MLP model, a neural network model. This result shows that the forecasting model. This result shows that the forecasting model a, pp.opriate to a prediction task should be selected based on characteristics of the time series data set. Analysis of the characteristics of the data set was performed by fractal analysis, measurement of Hurst index, and measurement of Lyapunov exponents. As a conclusion, a significant difference was not found in forecasting future events for the time series data which is deterministically chaotic, between a conventional forecasting model and a typical neural network model.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2009.05a
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• pp.408-411
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• 2009

In this paper, conventional PI, fuzzy neural network(FNN) and adaptive teaming mechanism(ALM)-FNN for rotor field oriented controlled(RFOC) induction motor are studied comparatively. The widely used control theory based design of PI family controllers fails to perform satisfactorily under parameter variation nonlinear or load disturbance. In high performance applications, it is useful to automatically extract the complex relation that represent the drive behaviour. The use of learning through example algorithms can be a powerful tool for automatic modelling variable speed drives. They can automatically extract a functional relationship representative of the drive behavior. These methods present some advantages over the classical ones since they do not rely on the precise knowledge of mathematical models and parameters. Comparative study of PI, FNN and ALM-FNN are carried out from various aspects which is dynamic performance, steady-state accuracy, parameter robustness and complementation etc. To have a clear view of the three techniques, a RFOC system based on a three level neutral point clamped inverter-fed induction motor drive is established in this paper. Each of the three control technique: PI, FNN and ALM-FNN, are used in the outer loops for rotor speed. The merit and drawbacks of each method are summarized in the conclusion part, which may a guideline for industry application.

• Journal of the Institute of Convergence Signal Processing
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• v.6 no.1
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• pp.15-22
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• 2005

In this paper, we propose a face recognition system by using the CCD color image. We first get the face candidate image by using YCbCr color model and adaptive skin color information. And we use it initial curve of active contour model to extract face region. We use the Eye map and mouth map using color information for extracting facial feature from the face image. To obtain center point of Log-polar image, we use extracted facial feature from the face image. In order to obtain feature vectors, we use extracted coefficients from DCT and wavelet transform. To show the validity of the proposed method, we performed a face recognition using neural network with BP learning algorithm. Experimental results show that the proposed method is robuster with higher recogntion rate than the conventional method for the rotation and scale variant.

• Safety and Health at Work
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• v.9 no.3
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• pp.265-276
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• 2018

Background: Resilience engineering is a paradigm for safety management that focuses on coping with complexity to achieve success, even considering several conflicting goals. Modern sociotechnical systems have to be resilient to comply with the variability of everyday activities, the tight-coupled and under-specified nature of work, and the nonlinear interactions among agents. At organizational level, resilience can be described as a combination of four cornerstones: monitoring, responding, learning, and anticipating. Methods: Starting from these four categories, this article aims at defining a semiquantitative analytic framework to measure organizational resilience in complex sociotechnical systems, combining the resilience analysis grid and the analytic hierarchy process. Results: This article presents an approach for defining resilience abilities of an organization, creating a structured domain-dependent framework to define a resilience profile at different levels of abstraction, and identifying weaknesses and strengths of the system and potential actions to increase system's adaptive capacity. An illustrative example in an anesthesia department clarifies the outcomes of the approach. Conclusion: The outcome of the resilience analysis grid, i.e., a weighed set of probing questions, can be used in different domains, as a support tool in a wider Safety-II oriented managerial action to bring safety management into the core business of the organization.