• Smart Structures and Systems
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• v.15 no.1
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• pp.81-97
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• 2015

Recently, a new output-only modal identification method based on time-frequency independent component analysis (ICA) has been developed by the authors and shown to be useful for even highly-damped structures. In many cases, it is of interest to identify the complex modes of structures with non-proportional damping. This study extends the time-frequency ICA based method to a complex ICA formulation for output-only modal identification of non-proportionally-damped structures. The connection is established between complex ICA model and the complex-valued modal expansion with sparse time-frequency representation, thereby blindly separating the measured structural responses into the complex mode matrix and complex-valued modal responses. Numerical simulation on a non-proportionally-damped system, laboratory experiment on a highly-damped three-story frame, and a real-world highly-damped base-isolated structure identification example demonstrate the capability of the time-frequency complex ICA method for identification of structures with complex modes in a straightforward and efficient manner.

• Journal of the Chungcheong Mathematical Society
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• v.21 no.3
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• pp.403-411
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• 2008

In 2005, A. Saxena, B. Soh and S. Priymak [10] proposed a two-flow blind identification protocol. But it has a weakness of the active-intruder attack and uses the pairing operation that causes slow implementation in smart cards. In 2008, Y. W. Lee [9] made a method of the active-intruder attack on their identification scheme and proposed a new zero-knowledge blind identification protocol for smart cards. In this paper, we give more simple and fast protocols than above protocols such that the prover using computationally limited devices such as smart cards has no need of computing the bilinear pairings. Computing the bilinear pairings is needed only for the verifier and is secure assuming the hardness of the Discrete-Logarithm Problem (DLP).

• ETRI Journal
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• v.34 no.1
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• pp.130-133
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• 2012

Recently, several noise-robust adaptive multichannel LMS algorithms have been proposed based on the spectral flatness of the estimated channel coefficients in the presence of additive noise. In this work, we propose a general form for the algorithms that integrates the existing algorithms into a common framework. Computer simulation results are presented and demonstrate that a new proposed algorithm gives better performance compared to existing algorithms in noisy environments.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.5
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• pp.1191-1199
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• 2016

To ensure the fairness, journal reviewers use blind-review system which hides the author information of the journal. Even though the author information is blinded, we could identify the author by looking at the field of the journal or containing words and phrases in the text. In this paper, we collected 315 journals of 20 authors and extracted text data. Bag-of-words were generated after preprocessing and used as an input of artificial neural network. The experiment shows the possibility of circumventing the blind review through identifying the author of the journal. By the experiment, we demonstrate the limitation of the current blind-review system and emphasize the necessity of robust blind-review system.

• Journal of the Chungcheong Mathematical Society
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• v.20 no.4
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• pp.355-366
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• 2007

In this paper, we propose a new blind group identification protocol and a hidden group signature protocol as its application. These protocols involve many provers and one verifier such that (1) the statement of all the provers are proved simultaneously, (2) and also all the provers using computationally limited devices (e.g. smart cards) have no need of computing the bilinear pairings, (3) but only the verifier uses the bilinear pairings. A. Saxena et al. proposed a two-round blind (group) identification protocol in 2005 using the bilinear pairings. But it reveals weakness in the active-intruder attack, and all the provers as well as the verifier must have devices computing bilinear pairings. Comparing their results, our protocol is secure from the active-intruder attack and has more fit for smart cards. In particular, it is secure under only the assumption of the hardness of the Discrete-Logarithm Problem in bilinear groups.

• Journal of applied mathematics & informatics
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• v.29 no.3_4
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• pp.869-877
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• 2011

In this paper, we propose an efficient two-flow zero-knowledge blind identification protocol on the elliptic curve cryptographic (ECC) system. A. Saxena et al. first proposed a two-flow blind identification protocol in 2005. But it has a weakness of the active-intruder attack and uses the pairing operation that causes slow implementation in smart cards. But our protocol is secure under such attacks because of using the hash function. In particular, it is fast because we don't use the pairing operation and consists of only two message flows. It does not rely on any underlying signature or encryption scheme. Our protocol is secure assuming the hardness of the Discrete-Logarithm Problem in bilinear groups.

• Smart Structures and Systems
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• v.17 no.3
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• pp.391-411
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• 2016

This study investigates the mode identifiability of a multi-span cable-stayed bridge in terms of a benchmark study using stabilization diagrams of a system model identified using stochastic subspace identification (SSI). Cumulative contribution ratios (CCRs) estimated from singular values of system models under different wind conditions were also considered. Observations revealed that wind speed might influence the mode identifiability of a specific mode of a cable-stayed bridge. Moreover the cumulative contribution ratio showed that the time histories monitored during strong winds, such as those of a typhoon, can be modeled with less system order than under weak winds. The blind data Acc 1 and Acc 2 were categorized as data obtained under a typhoon. Blind data Acc 3 and Acc 4 were categorized as data obtained under wind conditions of critical wind speeds around 7.5 m/s. Finally, blind data Acc 5 and Acc 6 were categorized as data measured under weak wind conditions.

• Structural Engineering and Mechanics
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• v.17 no.6
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• pp.819-828
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• 2004

A new process for estimating the natural frequency and the corresponding damping ratio in large structures is discussed. In a practical situation, it is very difficult to analyze large structures precisely because they are too complex to model using the finite element method and too heavy to excite using the exciting force method; in particular, the measured signals are seriously influenced by ambient noise. In order to identify the structural impulse response associated with the information of natural frequency and the corresponding damping ratio in large structures, the analysis process, a so-called "multiresolution blind system identification algorithm" which combines Mallat algorithm and the bicepstrum method. High time-frequency concentration is attained and the phase information is kept. The experimental result has demonstrated that the new analysis process exploiting the natural frequency and the corresponding damping ratio of structural response are useful tools in structural analysis application.

• Smart Structures and Systems
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• v.13 no.2
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• pp.257-280
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• 2014

In this paper, a novel PARAllel FACtor (PARAFAC) decomposition based Blind Source Separation (BSS) algorithm is proposed for modal identification of structures equipped with tuned mass dampers. Tuned mass dampers (TMDs) are extremely effective vibration absorbers in tall flexible structures, but prone to get de-tuned due to accidental changes in structural properties, alteration in operating conditions, and incorrect design forecasts. Presence of closely spaced modes in structures coupled with TMDs renders output-only modal identification difficult. Over the last decade, second-order BSS algorithms have shown significant promise in the area of ambient modal identification. These methods employ joint diagonalization of covariance matrices of measurements to estimate the mixing matrix (mode shape coefficients) and sources (modal responses). Recently, PARAFAC BSS model has evolved as a powerful multi-linear algebra tool for decomposing an $n^{th}$ order tensor into a number of rank-1 tensors. This method is utilized in the context of modal identification in the present study. Covariance matrices of measurements at several lags are used to form a $3^{rd}$ order tensor and then PARAFAC decomposition is employed to obtain the desired number of components, comprising of modal responses and the mixing matrix. The strong uniqueness properties of PARAFAC models enable direct source separation with fine spectral resolution even in cases where the number of sensor observations is less compared to the number of target modes, i.e., the underdetermined case. This capability is exploited to separate closely spaced modes of the TMDs using partial measurements, and subsequently to estimate modal parameters. The proposed method is validated using extensive numerical studies comprising of multi-degree-of-freedom simulation models equipped with TMDs, as well as with an experimental set-up.

• Journal of Korea Multimedia Society
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• v.19 no.11
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• pp.1837-1851
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• 2016

Newspaper reader mobile applications using text-to-speech (TTS) function enable blind people to read newspaper contents. But, tables cannot be easily read by the reader program because most of the tables are stored as images in the contents. Even though we try to use OCR (Optical character reader) programs to recognize letters from the table images, it cannot be simply applied to the table reading function because the table structure is unknown to the readers. Therefore, identification of exact location of each table cell that contains the text of the table is required beforehand. In this paper, we propose an efficient image processing algorithm to recognize all the cells in tables by identifying columns and rows in table images. From the cell location data provided by the table column and row identification algorithm, we can generate table structure information and table reading scenarios. Our experimental results with table images found commonly in newspapers show that our cell identification approach has 100% accuracy for simple black and white table images and about 99.7% accuracy for colored and complicated tables.