• Wind and Structures
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• v.6 no.6
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• pp.451-470
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• 2003

The classical two-degree-of-freedom (2-d-o-f) "sectional model" is of common use to study the dynamics of suspension bridges. It takes into account the first pair of vertical and torsional modes of the bridge and describes well global oscillations caused by wind actions on the deck, yielding very useful information on the overall behaviour and the aerodynamic and aeroelastic response; however, it does not consider relative oscillations between main cables and deck. On the contrary, the 4-d-o-f model described in the two Parts of this paper includes longitudinal deformability of the hangers (assumed linear elastic in tension and unable to react in compression) and thus allows to take into account not only global oscillations, but also relative oscillations between main cables and deck. In particular, when the hangers go slack, large nonlinear oscillations are possible; if the hangers remain taut, the oscillations remain small and essentially linear: the latter behaviour has been the specific object of Part I (Sepe and Augusti 2001), while the present Part II investigates the nonlinear behaviour (coexisting large and/or small amplitude oscillations) under harmonic actions on the cables and/or on the deck, such as might be generated by vortex shedding. Because of the discontinuities and strong nonlinearity of the governing equations, the response has been investigated numerically. The results obtained for sample values of mechanical and forcing parameters seems to confirm that relative oscillations cannot a priori be excluded for very long span bridges under wind-induced loads, and they can stimulate a discussion on the actual possibility of such phenomena.

• Journal of Biomedical Engineering Research
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• v.30 no.5
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• pp.373-380
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• 2009

In general, Independent component analysis (ICA) is a statistical blind source separation technique, used either in spatial or temporal domain. The spatial or temporal ICAs are designed to extract maximally independent sources in respective domains. The underlying sources for spatiotemporal data (sequence of images) can not always be guaranteed to be independent, therefore spatial ICA extracts the maximally independent spatial sources, deteriorating the temporal sources and vice versa. For such data types, spatiotemporal ICA tries to create a balance by simultaneous optimization in both the domains. However, the spatiotemporal ICA suffers the problem of source ambiguity. Recently, constrained ICA (c-ICA) has been proposed which incorporates a priori information to extract the desired source. In this study, we have extended the c-ICA for better analysis of spatiotemporal data. The proposed algorithm, i.e., constrained spatiotemporal ICA (constrained st-ICA), tries to find the desired independent sources in spatial and temporal domains with no source ambiguity. The performance of the proposed algorithm is tested against the conventional spatial and temporal ICAs using simulated data. Furthermore, its performance for the real spatiotemporal data, functional magnetic resonance images (fMRI), is compared with the SPM (conventional fMRI data analysis tool). The functional maps obtained with the proposed algorithm reveal more activity as compared to SPM.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.7
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• pp.296-303
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• 2007

Color quantization replaces the color of each pixel with the closest representative color, and thus it makes the resulting image partitioned into uniformly-colored regions. As a consequence, continuous, detailed variations of color over the corresponding regions in the original image are lost through color quantization. In this paper. we present a novel blind scheme for restoring such variations from a color-quantized input image without it priori knowledge of the quantization method. Our scheme identifies which pairs of uniformly-colored regions in the input image should have continuous variations of color in the resulting image. Then, such regions are seamlessly stitched through optimization while preserving the closest representative colors. The user can optionally indicate which regions should be separated or stitched by scribbling constraint brushes across the regions. We demonstrate the effectiveness of our approach through diverse examples, such as photographs, cartoons, and artistic illustrations.

• Journal of Korea Water Resources Association
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• v.54 no.1
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• pp.1-13
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• 2021

The Bayesian method is a very useful statistical tool in various fields including water resources. Therefore, in this study, the background of the Bayesian statistics and its application to the water resources field are reviewed. First, the history of the Bayesian method from the birth to the present, and the achievements of Bayesian statisticians are summarized. Next, the derivation of the Bayes' theorem, which is the basis of the Bayesian method, is presented, and the roles of the three elements of the Bayes' theorem: priori distribution, likelihood function, and posteriori distribution are explained. In addition, the unique features and advantages of the Bayesian statistics are summarized. Finally, the cases in water resources where the Bayesian method is applied are summarized by dividing them into several categories. With a prevalence of information and big data in the future, the Bayesian method is expected to be used more actively in the water resources field.

• Journal of the Korea Convergence Society
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• v.10 no.10
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• pp.153-160
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• 2019

In this study, The purpose of this study is to analyze how the rating scale and review contents attributes of social network-based services and products affect consumer purchasing patterns. information provided by screening the main factors. These analyzes are closely and quickly integrated between individuals and businesses, and enable to analyze the transaction that the impact of changing consumers on consumption and purchasing through the usefulness and a priori estimates of reviews and ratings at this time when networks and smart technologies are involved in a wide range of consumer activities. For this study, hierarchical analysis (AHP) and delphi (Delphi) methods applied to classify the high end variables into usefulness, technicality and value, Each subvariable was grouped into three factors and analyzed for importance through evaluation weights. As a result, we could analyze the importance of durability, usefulness, technological innovation, and cost and quality of value. Therefore, this study is expected to provide supplementary and additional useful information to consumers and companies participating in economic activities in various ways by simultaneously analyzing the review score and the reliability of posting information provided by verifying the main factors.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.3
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• pp.497-505
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• 2015

Existing Wi-Fi networks require users to follow network settings of the AP (Access Point), resulting in inconveniences for users, and the password of the AP is shared by all users connected to the AP, causing security information leaks as time goes by. We propose, in this work, a personalized secure Wi-Fi network, in which each user is assigned her own virtual Wi-Fi network. One virtual Wi-Fi per user makes the user-centric network configuration possible. A user sets a pair of her own SSID and password on her device a priori, and the AP publishes its public key in a suitable way. The AP also maintains an open Wi-Fi channel, to which users can connect anytime. On user's request, the user device sends a connection request message containing a pair of SSID and password encrypted with the AP's public key. Receiving the connection request message, the AP instantiates a new virtual AP secured with the pair of SSID and password, which is dedicated to that single user device. This virtual network is securer because the password is not shared among users. It is more convenient because the network adapts itself to the user device. Experiments show that these advantages are obtained with negligible degradation in the throughput performance.

• The KIPS Transactions:PartD
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• v.16D no.3
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• pp.451-462
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• 2009

Collaborative Filtering (CF), one of the most successful technologies among recommender systems, is a system assisting users in easily finding the useful information. However, despite its success and popularity, CF encounters a serious limitation with quality evaluation, called cold start problems. To alleviate this limitation, in this paper, we propose a unique method of building models derived from explicit ratings and applying the models to CF recommender systems. The proposed method is divided into two phases, an offline phase and an online phase. First, the offline phase is a building pre-computed model phase in which most of tasks can be conducted. Second, the online phase is either a prediction or recommendation phase in which the models are used. In a model building phase, we first determine a priori predicted rating and subsequently identify prediction errors for each user. From this error information, an error-reflected model is constructed. The error-reflected model, which is reflected average prior prediction errors of user neighbors and item neighbors, can make accurate predictions in the situation where users or items have few opinions; this is known as the cold start problems. In addition, in order to reduce the re-building tasks, the error-reflected model is designed such that the model is updated effectively and users'new opinions are reflected incrementally, even when users present a new rating feedback.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.10
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• pp.7061-7070
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• 2015

Multi-Response Surface Optimization aims at finding the optimal setting of input variables considering multiple responses simultaneously. The Weighted Mean Squared Error (WMSE) minimization approach, which imposes a different weight on the two components of mean squared error, squared bias and variance, first obtains WMSE for each response and then minimizes all the WMSEs at once. Most of the methods proposed for the WMSE minimization approach to date are classified into the prior preference articulation approach, which requires that a decision maker (DM) provides his/her preference information a priori. However, it is quite difficult for the DM to provide such information in advance, because he/she cannot experience the relationships or conflicts among the responses. To overcome this limitation, this paper proposes a posterior preference articulation method to the WMSE minimization approach. The proposed method first generates all (or most) of the nondominated solutions without the DM's preference information. Then, the DM selects the best one from the set of nondominated solutions a posteriori. Its advantage is that it provides an opportunity for the DM to understand the tradeoffs in the entire set of nondominated solutions and effectively obtains the most preferred solution suitable for his/her preference structure.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.24 no.1
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• pp.55-63
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• 2013

Digital retrodirective antenna system is easy to modify and upgrade because it can control the phase information of the output signal toward opposite direction to input signal without a priori knowledge of the arrival direction. Due to this advantage, it is possible to do fast beam tracking. Especially, we need to design the digital PLL performance for the digital retrodirective array antenna system. So, in this paper the settling time of phase estimator and BER performance of retrodirective antenna system are investigated according to design of filter in digital PLL. When QAM signal is used for 1 Mbps with $30^{\circ}$ of phase delay, simulation results show that digital phase conjugation technique has better BER performance by about 1 dB than non-phase conjugation system when digital filter is stable. If not, the system can't estimate the exact phase because of oscillation of filter.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.4
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• pp.438-444
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• 2004

The intelligent robots that will be needed in the near future are human-friendly robots that are able to coexist with humans and support humans effectively. To realize this, robots need to recognize their position and posture in known environment as well as unknown environment. Moreover, it is necessary for their localization to occur naturally. It is desirable for a robot to estimate of his position by solving uncertainty for mobile robot navigation, as one of the best important problems. In this paper, we describe a method for the localization of a mobile robot using image information of a moving object. This method combines the observed position from dead-reckoning sensors and the estimated position from the images captured by a fixed camera to localize a mobile robot. Using the a priori known path of a moving object in the world coordinates and a perspective camera model, we derive the geometric constraint equations which represent the relation between image frame coordinates for a moving object and the estimated robot's position. Since the equations are based or the estimated position, the measurement error may exist all the time. The proposed method utilizes the error between the observed and estimated image coordinates to localize the mobile robot. The Kalman filter scheme is applied for this method. its performance is verified by the computer simulation and the experiment.