• International Journal of Contents
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• v.9 no.4
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• pp.11-15
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• 2013

"Protein Folding Problem" is considered to be one of the "Great Challenges of Computer Science" and prediction of disordered protein is an important part of the protein folding problem. Machine learning models can predict the disordered structure of protein based on its characteristic of "learning from examples". Among many machine learning models, we investigate the possibility of multilayer perceptron (MLP) as the predictor of protein disorder. The investigation includes a single hidden layer MLP, multi hidden layer MLP and the hierarchical structure of MLP. Also, the target node cost function which deals with imbalanced data is used as training criteria of MLPs. Based on the investigation results, we insist that MLP should have deep architectures for performance improvement of protein disorder prediction.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.485-487
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• 1999

We propose the algorithm with which one can solve the problem of the two-level hierarchical optimal control of nonlinear systems by repeatedly updating the state vectors using the haar function and Picard's iteration methods. Using the simple operation of the coefficient vectors from the fast haar transformation in the upper level and applying that vectors to Picard iteration methods in the independently lower level allow us to obtain the another method except the inversion matrix operation of the high dimention and the kronecker product in the optimal control algorithm.

• Journal of the Korean Statistical Society
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• v.32 no.4
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• pp.425-448
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• 2003

Meta-analysis refers to quantitative methods for combining results from independent studies in order to draw overall conclusions. We consider hierarchical models including selection models under a skewed heavy tailed error distribution proposed originally by Chen et al. (1999) and Branco and Dey (2001). These rich classes of models combine the information of independent studies, allowing investigation of variability both between and within studies, and incorporate weight function. Here, the testing for the skewness parameter is discussed. The score test statistic for such a test can be shown to be expressed as the posterior expectations. Also, we consider the detail computational scheme under skewed normal and skewed Student-t distribution using MCMC method. Finally, we introduce one example from Johnson (1993)'s real data and apply our proposed methodology. We investigate sensitivity of our results under different skewed errors and under different prior distributions.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.579-581
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• 1988

This paper proposes the logic simulation for hierarchical logic network with function descriptor base data structure and algorithm on which a macro cell is considered as a logic elements. Function descriptor base data structure is useful when many logic elements of which type is same exist in a network, for it lessens the computer memory size used during the simulation. And the proposed simulation algorithm may improve the simulation speed.

• Korean Journal of Computational Design and Engineering
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• v.6 no.3
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• pp.157-168
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• 2001

The description of enterprise activities is the basis fur process improvement and information system building. To describe such activities, it is necessary to model the enterprise activities from the abstraction level to the implementation level in a stepwise and integrated form. For this reason, several modeling approaches have been proposed. However, most of them lacked the stepwise or integration aspects although some of them covered overall levels. This study proposes the hierarchical modeling approach for integrating the enterprise activity model from the abstraction level to the implementation level systematically. It is composed of five modeling levels such as function level, process level, task level, document workflow level, and event flow level. This study discusses the definition and characteristics of each level and compare our modeling frame with other modeling methodologies in case study.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.46
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• pp.173-180
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• 1998

In conventional probability-based quality evaluation of products with qualitative characteristics, many factors that affect the evaluation are not easily represented quantitatively, because the relation between reliability of human evaluator and each of these factors is not clear. In order to evaluate the quality of product with qualitative characteristics quantitatively, in this paper, the relation is represented as the shape of possibility distribution function of fuzzy set on the interval [0,1]. Furthermore, fuzzy reasoning is used to obtain the estimates of quality characteristics. And, it is supposed that many quality characteristics affected by the above factors are connected with the final characteristic through hierarchical structures. Finally, using the estimates gained from the final evaluation, qualitative characteristics are evaluated by use of concept of pattern recognition.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37A no.9
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• pp.728-737
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• 2012

An analytical method for identifying terrestrial broadcasting coverages is proposed for a terrestrial broadcasting transmission system adopting a hierarchical modulation technique. Bit Error Rates(BERs) are derived for hierarchically modulated non-uniform QAM constellations based on the Q-function analysis in AWGN environments. The derived BERs depend on the hierarchical modulation factor ${\alpha}$(HMF) and could be mapped to the broadcasting coverages according to the link budget analysis based on the log-distance path loss model. Finally the broadcasting coverage ratios for high priority(HP) streams and low priority(LP) streams are calculated and presented for the determination of the HMF.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1516-1529
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• 2023

In this paper, a motor gearbox fault-detection system based on a hierarchical flow-based model is proposed. The proposed system is used for the anomaly detection of a motion sound-based actuator module. The proposed flow-based model, which is a generative model, learns by directly modeling a data distribution function. As the objective function is the maximum likelihood value of the input data, the training is stable and simple to use for anomaly detection. The operation sound of a car's side-view mirror motor is converted into a Mel-spectrogram image, consisting of a folding signal and an unfolding signal, and used as training data in this experiment. The proposed system is composed of an encoder and a decoder. The data extracted from the layer of the pretrained feature extractor are used as the decoder input data in the encoder. This information is used in the decoder by performing an interlayer cross-scale convolution operation. The experimental results indicate that the context information of various dimensions extracted from the interlayer hierarchical data improves the defect detection accuracy. This paper is notable because it uses acoustic data and a normalizing flow model to detect outliers based on the features of experimental data.

• ETRI Journal
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• v.40 no.2
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• pp.227-236
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• 2018

Ultra-dense small cell networks (UD-SCNs) have been identified as a promising scheme for next-generation wireless networks capable of meeting the ever-increasing demand for higher transmission rates and better quality of service. However, UD-SCNs will inevitably suffer from severe interference among the small cell base stations, which will lower their spectral efficiency. In this paper, we propose a software-defined networking (SDN)-based hierarchical agglomerative clustering (SDN-HAC) framework, which leverages SDN to centrally control all sub-channels in the network, and decides on cluster merging using a similarity criterion based on a suitability function. We evaluate the proposed algorithm through simulation. The obtained results show that the proposed algorithm performs well and improves system payoff by 18.19% and 436.34% when compared with the traditional network architecture algorithms and non-cooperative scenarios, respectively.

• International Journal of Automotive Technology
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• v.8 no.3
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• pp.351-359
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• 2007

In this study, a hierarchical switching control scheme based on robust control theory is proposed for tracking control of vehicle longitudinal acceleration in the presence of large uncertainties. A model set consisting of four multiplicative-uncertainty models is set up, and its corresponding controller set is designed by the LMI approach, which can ensures the robust performance of the closed loop system under arbitray switching. Based on the model set and the controller set, a switching index function by estimating the system gain of the uncertainties between the plant and the nominal model is designed to determine when and which controller should be switched into the closed loop. After theoretical analyses, experiments have also been carried out to validate the proposed control algorithm. The results show that the control system has good performance of robust stability and tracking ability in the presence of large uncertainties. The response time is smaller than 1.5s and the max tracking error is about $0.05\;m/S^2$ with the step input.