• Korean Journal of Cognitive Science
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• v.18 no.4
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• pp.399-415
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• 2007

Many objects are always present in a visual scene. Since the visual system has limited capacity to process multiple stimuli at a time, how to cope with this informational overload is one of the important problems to solve in visual perception. This study investigated the suppressive interactions among multiple stimuli when attention was directed to either one of the stimuli or all of them. The results indicate that suppressive interactions among multiple circles were reduced in V4 when subjects paid attention to one of the four locations, as compared to the unattended condition. However, suppressive interactions were not reduced when they paid attention to all four items as a set, in order to compute their mean size. These results suggest that whereas focused attention serves to later out irrelevant information, distributed attention provides an average representation of multiple stimuli.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.4929-4947
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• 2017

In visual object tracking, Correlation Filter-based Tracking (CFT) systems have arouse recently to be the most accurate and efficient methods. The CFT's circularly shifts the larger search window to find most likely position of the target. The need of larger search window to cover both background and object make an algorithm sensitive to the background and the target occlusions. Further, the use of fixed-sized windows for training makes them incapable to handle scale variations during tracking. To address these problems, we propose two layer target representation in which both global and local appearances of the target is considered. Multiple local patches in the local layer provide robustness to the background changes and the target occlusion. The target representation is enhanced by employing additional reversed RGB channels to prevent the loss of black objects in background during tracking. The final target position is obtained by the adaptive weighted average of confidence maps from global and local layers. Furthermore, the target scale variation in tracking is handled by the statistical model, which is governed by adaptive constraints to ensure reliability and accuracy in scale estimation. The proposed structural enhancement is tested on VTBv1.0 benchmark for its accuracy and robustness.

• The KIPS Transactions:PartB
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• v.11B no.1
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• pp.53-62
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• 2004

The processing of emotional information is very important in Human-Computer Interaction (HCI). In particular, it is very important in video information processing to deal with a user's affection. To handle emotional information, it is necessary to represent meaningful features and detect them efficiently. Even though it is not an easy task to detect emotional events from low level features such as colour and motion, it is possible to detect them if we use statistical analysis like Linear Discriminant Analysis (LDA). In this paper, we propose a representation scheme for emotion-related features and a defection method. We experiment with extracted features from video to detect emotional events and obtain desirable results.

• Wind and Structures
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• v.12 no.6
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• pp.505-517
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• 2009

Synchronous wind-induced pressures, measured in wind-tunnel tests on model buildings instrumented with hundreds of pressure taps, are an invaluable resource for designing safe buildings efficiently. They enable a much more detailed, accurate representation of the forces and moments that drive engineering design than conventional tables and graphs do. However, the very large volumes of data that such tests typically generate pose a challenge to their widespread use in practice. This paper explains how a wavelet representation for the time series of pressure measurements acquired at each tap can be used to compress the data drastically while preserving those features that are most influential for design, and also how it enables incremental data transmission, adaptable to the accuracy needs of each particular application. The loss incurred in such compression is tunable and known. Compression rates as high as 90% induce distortions that are statistically indistinguishable from the intrinsic variability of wind-tunnel testing, which we gauge based on an unusually large collection of replicated tests done under the same wind-tunnel conditions.

• The Korean Journal of Applied Statistics
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• v.31 no.6
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• pp.707-719
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• 2018

Various time series representation methods have been proposed for efficient time series clustering and classification. Lin et al. (DMKD, 15, 107-144, 2007) proposed a symbolic aggregate approximation (SAX) method based on symbolic representations after approximating the original time series using piecewise local mean. The performance of SAX therefore depends heavily on how well the piecewise local averages approximate original time series features. SAX equally divides the entire series into an arbitrary number of segments; however, it is not sufficient to capture key features from complex, large-scale time series data. Therefore, this paper considers data-adaptive local constant approximation of the time series using the unbalanced Haar wavelet transformation. The proposed method is shown to outperforms SAX in many real-world data applications.

• Communications for Statistical Applications and Methods
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• v.28 no.3
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• pp.295-305
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• 2021

Most graphical representation methods for two-dimensional contingency tables are based on the frequencies, probabilities, association measures, and goodness-of-fit statistics. In this work, a method is proposed to represent the correlation coefficients for each of the two selected levels of the row and column variables. Using the correlation coefficients, one can obtain the vector-matrix that represents the angle corresponding to each cell. Thus, these vectors are represented as a unit circle with angles. This is called a CC plot, which is a correlation plot for a contingency table. When the CC plot is used with other graphical methods as well as statistical models, more advanced analyses including the relationship among the cells of the row or column variables could be derived.

• The Korean Journal of Applied Statistics
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• v.22 no.5
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• pp.943-960
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• 2009

This study suggests an algorithm for generating TMY(typical meteorological year) for the Korean peninsula, and generates the TMY based on the algorithm using 11 years(1998~2008) wind data observed at 77 sites of Regional Meteorological Offices(RMO). The algorithm consists of computing TMM scores based on the various statistics defined by the Fikenstein-Shafer statistical model and, in turn, generating TMY based on the TMM scores. Also the algorithm has two stages designed to yield the best representation of the regional wind characteristics appeared during the 11 years(1998~2008). The first stage is designed for the representation of each of 77 regions of RMO and the second is for the Korean peninsula. Various comparison studies are provided to demonstrate the properties of the TMY like its utility and typicality.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.402-410
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• 2017

For many years, researchers in face description area have been representing and recognizing faces based on different methods that include subspace discriminant analysis, statistical learning and non-statistics based approach etc. But still automatic face recognition remains an interesting but challenging problem. This paper presents a novel and efficient face image representation method based on Optimized Local Ternary Pattern (OLTP) texture features. The face image is divided into several regions from which the OLTP texture feature distributions are extracted and concatenated into a feature vector that can act as face descriptor. The recognition is performed using nearest neighbor classification method with Chi-square distance as a similarity measure. Extensive experimental results on Yale B, ORL and AR face databases show that OLTP consistently performs much better than other well recognized texture models for face recognition.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.613-616
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• 2018

Sequence tagging is an important task in Natural Language Processing (NLP), in which the Named Entity Recognition (NER) is the key issue. So far the most widely adopted model for NER in NLP is that of combining the neural network of bidirectional long short-term memory (BiLSTM) and the statistical sequence prediction method of Conditional Random Field (CRF). In this work, we improve the prediction accuracy of the BiLSTM by supporting an aligned word representation mechanism. We have performed experiments on multilingual (English, Spanish and Dutch) datasets and confirmed that our proposed model outperformed the existing state-of-the-art models.

• The Korean Journal of Applied Statistics
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• v.25 no.6
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• pp.925-932
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• 2012

The article introduces a method to estimate a cointegrated vector autoregressive model, using mixed-frequency data, in terms of a state-space representation of the vector error correction(VECM) of the model. The method directly estimates the parameters of the model, in a state-space form of its VECM representation, using the available data in its mixed-frequency form. Then it allows one to compute in-sample smoothed estimates and out-of-sample forecasts at their high-frequency intervals using the estimated model. The method is applied to a mixed-frequency data set that consists of the quarterly real gross domestic product and three monthly coincident indicators. The result shows that the method produces accurate smoothed and forecasted estimates in comparison to a method based on single-frequency data.