• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.3-6
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• 2000

In this paper, ‘gesture’ is discussed as a means of human-friendly communication between man and machine. We classify various gestures into two Categories: ‘contact based’ and ‘non-contact based’ Each method is reviewed and some real applications are introduced. Also, key design issues of the method are addressed and some contributions of soft-computing techniques, such as fuzzy logic, artificial neural networks (ANN), rough set theory and evolutionary computation, are discussed.

• Journal of the Korean Institute of Intelligent Systems
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• v.3 no.2
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• pp.3-17
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• 1993

The fuzzy pattern matching technique has been developed in the framework of fuzzy set and possibility theory in order to take into account the imprecision and the uncertainty pervading values which have to be compared to requirements (which may be fuzzy) in a pattern matching process. This paper restates the basic principles and extends them to situations where (sub)patterns are only required to be satisfied up to a given tolerance (which may be fuzzy), or where the different subparts of a compound pattern may have various levels of importance. Both cases correspond to a weakening of elementary patterns. which can be expressed by a fuzzy relations modelling an approximate equality or an uncertain strict equality respectively. We also study the more sophisticated case where some elementary patterns have not to be satisfied with the highest priority provided that weaker requirements remain satisfied. The fuzzy pattern matching technique applies in a variety of problems including the evaluation of soft queries with respect to a fuzzy database, the evaluation of the fuzzy condition parts of rules in approximate reasoning, or the evaluation of the belonging of an ill-known object to a flexible class in classification problems.

• Management & Information Systems Review
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• v.3
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• pp.15-45
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• 1999

Processing techniques of remote sensed image data using computer have been recognized very necessary techniques to all social fields, such as, environmental observation, land cultivation, resource investigation, military trend grasp and agricultural product estimation, etc. Especially, accurate classification and analysis to remote sensed image da are important elements that can determine reliability of remote sensed image data processing systems, and many researches have been processed to improve these accuracy of classification and analysis. Traditionally, remote sensed image data processing systems have been processed 2 or 3 selected bands in multiple bands, in this time, their selection criterions are statistical separability or wavelength properties. But, it have be bring up the necessity of bands selection method by data distribution characteristics than traditional bands selection by wavelength properties or statistical separability. Because data sensing environments change from multispectral environments to hyperspectral environments. In this paper for efficient data classification in multispectral bands environment, a band feature extraction method using the Rough sets theory is proposed. First, we make a look up table from training data, and analyze the properties of experimental multispectral image data, then select the efficient band using indiscernibility relation of Rough set theory from analysis results. Proposed method is applied to LANDSAT TM data on 2 June 1992. From this, we show clustering trends that similar to traditional band selection results by wavelength properties, from this, we verify that can use the proposed method that centered on data properties to select the efficient bands, though data sensing environment change to hyperspectral band environments.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.11
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• pp.1202-1213
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• 2009

Muffler sound quality of a vehicle has widely been evaluated using commercial softwares. However, the commercial softwares are providing only simple sound quality index. To get better sound quality evaluation method of the muffler, a new approach is suggested based on the subjective sound quality test. The suggested subjective sound quality evaluation method is refined with the fuzzy theory. The developed method is applied for the evaluation of various kinds of vehicle muffler sounds.

• International Journal of Automotive Technology
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• v.3 no.3
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• pp.89-94
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• 2002

This paper presents an architecture for distributed control systems and its underlying methodological framework. Ideas and concepts of distributed systems, artificial intelligence, and soft computing are merged into a unique architecture to provide cooperation, flexibility, and adaptability required by knowledge processing in intelligent control systems. The distinguished features of the architecture include a local problem solving capability to handle the specific requirements of each part of the system, an evolutionary case-based mechanism to improve performance and optimize controls, the use of linguistic variables as means for information aggregation, and fuzzy set theory to provide local control. A distributed traffic control system application is discussed to provide the details of the architecture, and to emphasize its usefulness. The performance of the distributed control system is compared with conventional control approaches under a variety of traffic situations.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.502-506
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• 2013

The cash flow forecasting is normally done by contractors in construction industry at early stages of the project for contractual decisions. The decision making in such situations involve uncertainty about future cash flows and assessment of working capital requirements gains more importance in projects constrained by cash. The traditional approach to assess the working capital requirements is deterministic in and neglects the uncertainty. This paper presents an alternate approach to assessment of working capital requirements for contractor based on fuzzy set theory by considering the uncertainty and ambiguity involved at payment periods. Statistical methods are used to deal with the uncertainty for working capital curves. Membership functions of the fuzzy sets are developed based on these statistical measures. Advantage of fuzzy peak working capital requirements is demonstrated using peak working capital requirements curves. Fuzzy peak working capital requirements curves are compared with deterministic curves and the results are analyzed. Fuzzy weighted average methodology is proposed for the assessment of peak working capital requirements.

• Proceedings of the Korean Geotechical Society Conference
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• 2009.03a
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• pp.456-463
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• 2009

The preconsolidation pressure has been commonly determined by oedometer test. However, it can also be determined by in-situ test, such as piezocone test with theoretical and(or) empirical correlations. Recently, Neural Network(NN) theory was applied and some models were proposed to estimate the preconsolidation pressure or OCR. However, since the optimization process of synaptic weights of NN model is dependent on the initial synaptic weights, NN models which are trained with different initial weights can't avoid the variability on prediction result for new database even though they have same structure and use same transfer function. In this study, Committee Neural Network(CNN) model is proposed to improve the initial weight dependency of multi-layered neural network model on the prediction of preconsolidation pressure of soft clay from piezocone test result. It was found that even though the NN model has the optimized structure for given training data set, it still has the initial weight dependency, while the proposed CNN model can improve the initial weight dependency of the NN model and provide a consistent and precise inference result than existing NN models.

• Journal of the Society of Naval Architects of Korea
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• v.30 no.1
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• pp.87-93
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• 1993

The motion response of a ship moored in a rectangular harbor excited by long waves has been studied theoretically and experimentally. Experiments are performed in a shallow basin. A ship model is set moored by soft springs at various positions in a model harbor subjected to regular waves with period ranging from 2 to 3 seconds. Wave and ship responses are measured and compared with theory. It is found that theoretical results agree qualitatively with experimental results. The main source of quantitative discrepancies is presumably due to real fluid effects such as separation at the harbor entrance and friction on harbor boundaries.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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• pp.1193-1215
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• 2021

The measurement of the key product quality index plays an important role in improving the production efficiency and ensuring the safety of the enterprise. Since the actual working conditions and parameters will inevitably change to some extent with time, such as drift of working point, wear of equipment and temperature change, etc., these will lead to the degradation of the quality variable prediction model. To deal with this problem, the selective integrated moving windows based principal component regression (SIMV-PCR) is proposed in this study. In the algorithm of traditional moving window, only the latest local process information is used, and the global process information will not be enough. In order to make full use of the process information contained in the past windows, a set of local models with differences are selected through hypothesis testing theory. The significance levels of both T - test and χ² - test are used to judge whether there is identity between two local models. Then the models are integrated by Bayesian quality estimation to improve the accuracy of quality variable prediction. The effectiveness of the proposed adaptive soft measurement method is verified by a numerical example and a practical industrial process.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3498-3511
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• 2016

We investigate the channel state information (CSI) in multi-input multi-output (MIMO) cooperative networks that employ the amplify-and-forward transmission scheme. Least squares and expectation conditional maximization have been proposed in the system. However, neither of these two approaches takes advantage of channel sparsity, and they cause estimation performance loss. Unlike linear channel estimation methods, several compressed channel estimation methods are proposed in this study to exploit the sparsity of the MIMO cooperative channels based on the theory of compressed sensing. First, the channel estimation problem is formulated as a compressed sensing problem by using sparse decomposition theory. Second, the lower bound is derived for the estimation, and the MIMO relay channel is reconstructed via compressive sampling matching pursuit algorithms. Finally, based on this model, we propose a novel algorithm so called sparsity adaptive expectation maximization (SAEM) by using Kalman filter and expectation maximization algorithm so that it can exploit channel sparsity alternatively and also track the true support set of time-varying channel. Kalman filter is used to provide soft information of transmitted signals to the EM-based algorithm. Various numerical simulation results indicate that the proposed sparse channel estimation technique outperforms the previous estimation schemes.