• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.10C
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• pp.942-947
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• 2006

In this paper, a new feature representation technique called Improved 2-directional 2-dimensional direct linear discriminant analysis (Improved $(2D)^2$ DLDA) is proposed. In the case of face recognition, thesmall sample size problem and need for many coefficients are often encountered. In order to solve these problems, the proposed method uses the direct LDA and 2-directional image scatter matrix. Moreover the selection method of feature vector and the method of similarity measure are proposed. The ORL face database is used to evaluate the performance of the proposed method. The experimental results show that the proposed method obtains better recognition rate and requires lesser memory than the direct LDA.

• Asian Journal of Innovation and Policy
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• v.7 no.1
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• pp.178-189
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• 2018

In this paper, we present a case study of a new emerging business startup involved in smart house appliances. The irreversible investment concept and real-option theory are introduced as the fundamentals of the model. By using games theory we show that the startup's actions can trigger reactions from other firms. The first part covers initial the research and development stage, while the second part covers production and commercialization. The findings of this study suggest that, given a certain amount of initial investment, an open and shared innovation may lead to hurting a firm's investment while strengthening the competitors' position in the market. However, given the sensitivity analysis, when volatility and demand grow favorably, sharing R&D investment is not a bad option for a new player to adjust its position in the market while still maintaining positive returns.

• Proceedings of the Korean Radioactive Waste Society Conference
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• 2004.11a
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• pp.205-209
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• 2004

• Computers and Concrete
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• v.16 no.5
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• pp.723-740
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• 2015

In this study, the seismic performance of skewed highway bridges has been assessed by using fragility function methodology. Incremental Dynamic Analysis (IDA) has been used to prepare complete information about the different damage states of a 30 degree skewed highway bridge. A three dimensional model of a skewed highway bridge is presented and incremental dynamic analysis has been applied. The details of the full nonlinear procedures have also been presented. Different spectral intensity measures are studied and the effects of the period on the fragility curves are shown in different figures. The efficiency, practicality and proficiency of these different spectral intensity measures are compared. A suite of 20 earthquake ground motions are considered for nonlinear time history analysis. It has been shown that, considering different intensity measures (IM) leads us to overestimate or low estimate the damage probability which has been discussed completely.

• Steel and Composite Structures
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• v.37 no.2
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• pp.151-173
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• 2020

This paper aims to probabilistically evaluate performance of two types of I beam to box column (IBBC) connection. With the objective of considering the variability of seismic loading demand, statistical features of the inter-story drift ratio corresponding to the second, fifth and eleventh story of a 12-story steel special moment resisting frames are extracted through incremental dynamic analysis at global collapse state. Variability of geometrical variables and material strength are also taken into account. All of these random variables are exported as inputs to a probabilistic finite element model which simulates the connection. At the end, cumulative distribution functions of local seismic demand for each component of each connection are provided using histogram sampling. Through a parametric study on probabilistic local seismic demand, the influence of some geometrical random variables on the performance of IBBC connections is demonstrated. Furthermore, the probabilistic study revealed that IBBC connection with widened flange has a better performance than the un-widened flange. Also, a design procedure is proposed for WF connections to achieve a same connection performance in different stories.

• Journal of TMJ Balancing Medicine
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• v.3 no.1
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• pp.23-26
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• 2013

Objectives: Therapeutic effect of Yinyang Balancing Appliance of functional cerebrospinal therapy (FCST) for meridian and neurologic yinyang balance was observed in Iron Deficiency Anemia. Methods: One Iron Deficiency Anemia cases were managed with the Yinyang Balancing Appliance on temporomandibular joint (TMJ), combined with acupuncture. Clinical outcome measurement was based on subjective measures and clinical observations. Results: The patients showed positive changes even after the initial treatment and this effext maintained over the follow-up period. Conclusions: Although it is not clear the effect is a sustaining or temporary in its nature, a positive effect was observed and further clinical and biological research on FCST is expected.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.887-890
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• 1993

By introducing the notion of constraint-oriented fuzzy inference, we will show that it provides us ways of fuzzy control methods that has abilities of adaptation, learning and self-organization. The basic supporting techniques behind these abilities are“hard”processing by Artificial Intelligence or traditional computational framework and“soft”processing by Neural Network or Genetic Algorithm techniques. The reason that these techniques can be incorporated to fuzzy control systems is that the notion of“constraint”itself has two fundamental properties, that is, the“modularity”property due to its declarativeness and the“logicality”property due to its two-valuedness. From the former property, the modularity property, decomposing and integrating constraints can be done easily and efficiently, which enables us to carry out the above“soft”processing. From the latter property, the logicality property, Qualitative Reasoning and Instance Generalization by Symbolic Reasoning an be carried out, thus enabling the“hard”processing.

• Computers and Concrete
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• v.20 no.4
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• pp.419-427
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• 2017

A methodology, based on fragility functions, is proposed to evaluate the seismic performance of seismic isolated $45^{\circ}$ skewed concrete bridge: 1) twelve types of seismic isolation devices are considered based on two different design parameters 2) fragility functions of a three-span bridge with and without seismic isolation devices are analytically evaluated based on 3D nonlinear incremental dynamic analyses which seismic input consists of 20 selected ground motions. The optimum combinations of isolation device design parameters are identified comparing, for different limit states, the performance of 1) the Seismic Isolated Bridges (SIB) and 2) Not Seismic Isolated Bridge (NSIB) designed according to the AASHTO standards.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.2125-2133
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• 2018

To achieve high performance speed regulation, a robust adaptive speed controller is proposed for the permanent magnet synchronous motor (PMSM) subject to parameter uncertainties and input saturations in this paper. A nonlinear adaptive control is introduced to compensate the PMSM speed tracking errors due to uncertainties, disturbances and control input saturation constraints. By combining the adaptive control and the nonlinear robust control based on the interconnection and damping assignment (IDA) strategy, a new robust adaptive control is designed for speed regulation of PMSM. Stability and robustness of the closed-loop control system involved with the constrained control inputs rather than unconstrained control inputs are validated. Simulations for PMSM control in the presence of uncertainties and saturations nonlinearities show that the proposed approach is effective to regulate speed, and the average tracking error using the proposed approach is at least 32% smaller than the compared methods.

• Journal of information and communication convergence engineering
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• v.16 no.2
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• pp.106-113
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• 2018

Although deep learning methods of convolutional neural networks (CNNs) and long-/short-term memory (LSTM) are widely used for text categorization, they still have certain shortcomings. CNNs require that the text retain some order, that the pooling lengths be identical, and that collateral analysis is impossible; In case of LSTM, it requires the unidirectional operation and the inputs/outputs are very complex. Against these problems, we thus improved these traditional deep learning methods in the following ways: We created collateral CNNs accepting disorder and variable-length pooling, and we removed the input/output gates when creating bidirectional LSTMs. We have used four benchmark datasets for topic and sentiment classification using the new methods that we propose. The best results were obtained by combining LTSM regional embeddings with data convolution. Our method is better than all previous methods (including deep learning methods) in terms of topic and sentiment classification.