• Journal of Information Science Theory and Practice
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• v.9 no.1
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• pp.24-34
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• 2021

The popularity of social networking sites (SNS) has facilitated communication between users. The usage of SNS helps users in their daily life in various ways such as sharing of opinions, keeping in touch with old friends, making new friends, and getting information. However, some users misuse SNS to belittle or hurt others using profanities, which is typical in cyberbullying incidents. Thus, in this study, we aim to identify profane words from the ASKfm corpus to analyze the profane word distribution across four different roles involved in cyberbullying based on lexicon dictionary. These four roles are: harasser, victim, bystander that assists the bully, and bystander that defends the victim. Evaluation in this study focused on occurrences of the profane word for each role from the corpus. The top 10 common words used in the corpus are also identified and represented in a graph. Results from the analysis show that these four roles used profane words in their conversation with different weightage and distribution, even though the profane words used are mostly similar. The harasser is the first ranked that used profane words in the conversation compared to other roles. The results can be further explored and considered as a potential feature in a cyberbullying detection model using a machine learning approach. Results in this work will contribute to formulate the suitable representation. It is also useful in modeling a cyberbullying detection model based on the identification of profane word distribution across different cyberbullying roles in social networks for future works.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.545-554
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• 2018

As the map paradigm shifts from analog to digital, the LOD (Level Of Detail) of spatial information needs to be redefined. In this study, we propose 4- dimensional indoor LOD model which can be used in digital map environment. For this purpose, the limitation of the previous research is derived through study of related works, and based on this, four different LODs are defined such PLOD (Position accuracy LOD) based on position accuracy, GLOD (Geometric LOD) based on shape representation, CLOD (Complete LOD) based on generalization, and SLOD (Semantic LOD) based on theme accuracy. In addition, we describe the relationships among the four different LODs, and explain how to express the indoor LOD using the four different LODs and show examples. In the future, the case studies of indoor LOD adoption for various indoor services and the study of method for applying CLOD and SLOD to each feature should be performed to verify the feasibility and validity of proposed indoor LOD.

• Coupled systems mechanics
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• v.7 no.6
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• pp.649-668
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• 2018

In this paper, we present a numerical model for fluid-structure interaction between structure built of porous media and acoustic fluid, which provides both pore pressure inside porous media and hydrodynamic pressures and hydrodynamic forces exerted on the upstream face of the structure in an unified manner and simplifies fluid-structure interaction problems. The first original feature of the proposed model concerns the structure built of saturated porous medium whose response is obtained with coupled discrete beam lattice model, which is based on Voronoi cell representation with cohesive links as linear elastic Timoshenko beam finite elements. The motion of the pore fluid is governed by Darcy's law, and the coupling between the solid phase and the pore fluid is introduced in the model through Biot's porous media theory. The pore pressure field is discretized with CST (Constant Strain Triangle) finite elements, which coincide with Delaunay triangles. By exploiting Hammer quadrature rule for numerical integration on CST elements, and duality property between Voronoi diagram and Delaunay triangulation, the numerical implementation of the coupling results with an additional pore pressure degree of freedom placed at each node of a Timoshenko beam finite element. The second original point of the model concerns the motion of the outside fluid which is modeled with mixed displacement/pressure based formulation. The chosen finite element representations of the structure response and the outside fluid motion ensures for the structure and fluid finite elements to be connected directly at the common nodes at the fluid-structure interface, because they share both the displacement and the pressure degrees of freedom. Numerical simulations presented in this paper show an excellent agreement between the numerically obtained results and the analytical solutions.

• Journal of IKEEE
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• v.23 no.3
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• pp.910-916
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• 2019

Non-local similarity of natural images is one of highly exploited features in various applications dealing with images. Unique edges, texture, and pattern of the images are frequently repeated over the entire image. Once the similar image blocks are classified into a cluster, representative features of the image blocks can be extracted from the cluster. The bigger the size of the cluster is the better the additive white noise can be separated. Denoising is one of major research topics in the image processing field suppressing the additive noise. In this paper, a denoising algorithm is proposed which first clusters the noisy image blocks based on similarity, extracts the feature of the cluster, and finally recovers the original image. Performance experiments with several images under various noise strengths show that the proposed algorithm recovers the details of the image such as edges, texture, and patterns while outperforming the previous methods in terms of PSNR in removing the additive Gaussian noise.

• The Korean Journal of Archival Studies
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• no.39
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• pp.73-100
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• 2014

For a society to be constantly sustained, it needs its past memories to be made present into cultural memories. The state of oblivion resulted from the time difference of the past and the present is commonly made present into commemoration days or memorial spaces. Each has its own form of representation it reflects, but all are related to how past is remembered and how it will be formed. Records, especially the authentic records the nation secured, have an ontological feature of proof that is able to persuade the current present of the past present, and inherent in it is faith that originates from fundamental objectivity. Therefore the image of silence that such strictness of records creates realizes the detonation of specific memories through the act of visual exhibition. Hence, this paper endeavors to examine how the national records, particularly the photographical records, was utilized and how it influenced the people and the society after its organization, through analysing examples of memorial spaces.

• Smart Structures and Systems
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• v.23 no.6
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• pp.565-577
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• 2019

One of the most effective countermeasures for mitigating cable vibration is to install mechanical dampers near the anchorage of the cable. Most of the dampers used in the field are so-called passive dampers where their parameters cannot be changed once designed. The parameters of passive dampers are usually determined based on the optimal damper force obtained from the universal design curve for linear dampers, which will provide a maximum additional damping for the cable. As the optimal damper force is chosen based on a predetermined principal vibration mode, passive dampers will be most effective if cable undergoes single-mode vibration where the vibration mode is the same as the principal mode used in the design. However, in the actual engineering practice, multi-mode vibrations are often observed for cables. Therefore, it is desirable to have dampers that can suppress different modes of cable vibrations simultaneously. In this paper, MR dampers are proposed for controlling multi-mode cable vibrations, because of its ability to change parameters and its adaptability of active control without inquiring large power resources. Although the highly nonlinear feature of the MR material leads to a relatively complex representation of its mathematical model, effective control strategies can still be derived for suppressing multi-mode cable vibrations based on nonlinear modelling, as proposed in this paper. Firstly, the nonlinear Bouc-wen model is employed to accurately portray the salient characteristics of the MR damper. Then, the desired optimal damper force is determined from the universal design curve of friction dampers. Finally, the input voltage (current) of MR damper corresponding to the desired optimal damper force is calculated from the nonlinear Bouc-wen model of the damper using a piecewise linear interpolation scheme. Numerical simulations are carried out to validate the effectiveness of the proposed control algorithm for mitigating multi-mode cable vibrations induced by different external excitations.

• Journal of Naturopathy
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• v.11 no.2
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• pp.123-135
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• 2022

Background: The study aims to examine what color representation means to artists by investigating various hypotheses about van Gogh's expression of yellow and verifying the reason of his preference of yellow. Purpose: The purpose of this study is to investigate whether yellow expression of Vincent van Gogh is a result of physiological responses of alcoholism, expression feature of mental disorder, or a problem caused by psychological motivation. Methods: In order to verify the research question, we referred to research literatures that analyzed a characteristics of Gogh's works in a various area such as psychology and psychiatry, and Gogh's symptoms and his own skills. Results: The findings suggested that Gogh's yellow preference is related to the psychological factors such as inner motivation, not a xanthopsia which is brought about alcoholism and mental disorder. Conclusions: Gogh's Yellow expression is dominantly influenced by the psychological factors. Thus, it can say that the psychological factors has a great on characteristic of artist's color expression.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.4084-4104
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• 2021

Visual place recognition (VPR) is a fundamental yet challenging task of mobile robot navigation and localization. The existing VPR methods are usually based on some pairwise similarity of image descriptors, so they are sensitive to visual appearance change and also computationally expensive. This paper proposes a simple yet effective four-step method that achieves adaptive convolutional neural network (CNN) landmark matching for VPR. First, based on the features extracted from existing CNN models, the regions with higher significance scores are selected as landmarks. Then, according to the coordinate positions of potential landmarks, landmark matching is improved by removing mismatched landmark pairs. Finally, considering the significance scores obtained in the first step, robust image retrieval is performed based on adaptive landmark matching, and it gives more weight to the landmark matching pairs with higher significance scores. To verify the efficiency and robustness of the proposed method, evaluations are conducted on standard benchmark datasets. The experimental results indicate that the proposed method reduces the feature representation space of place images by more than 75% with negligible loss in recognition precision. Also, it achieves a fast matching speed in similarity calculation, satisfying the real-time requirement.

• Smart Structures and Systems
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• v.29 no.1
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• pp.181-193
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• 2022

Data-driven structural health monitoring (SHM) of civil infrastructure can be used to continuously assess the state of a structure, allowing preemptive safety measures to be carried out. Long-term monitoring of large-scale civil infrastructure often involves data-collection using a network of numerous sensors of various types. Malfunctioning sensors in the network are common, which can disrupt the condition assessment and even lead to false-negative indications of damage. The overwhelming size of the data collected renders manual approaches to ensure data quality intractable. The task of detecting and classifying an anomaly in the raw data is non-trivial. We propose an approach to automate this task, improving upon the previously developed technique of image-based pre-processing on one-dimensional (1D) data by enriching the features of the neural network input data with multiple channels. In particular, feature engineering is employed to convert the measured time histories into a 3-channel image comprised of (i) the time history, (ii) the spectrogram, and (iii) the probability density function representation of the signal. To demonstrate this approach, a CNN model is designed and trained on a dataset consisting of acceleration records of sensors installed on a long-span bridge, with the goal of fault detection and classification. The effect of imbalance in anomaly patterns observed is studied to better account for unseen test cases. The proposed framework achieves high overall accuracy and recall even when tested on an unseen dataset that is much larger than the samples used for training, offering a viable solution for implementation on full-scale structures where limited labeled-training data is available.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.4
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• pp.621-631
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• 2023

Adversarial training improves the robustness of deep neural networks for adversarial examples. However, the previous adversarial training method focuses only on the adversarial loss function, ignoring that even a small perturbation of the input layer causes a significant change in the hidden layer features. Consequently, the accuracy of a defended model is reduced for various untrained situations such as clean samples or other attack techniques. Therefore, an architectural perspective is necessary to improve feature representation power to solve this problem. In this paper, we apply an attention module that generates an attention map of an input image to a general model and performs PGD adversarial training upon the augmented model. In our experiments on the CIFAR-10 dataset, the attention augmented model showed higher accuracy than the general model regardless of the network structure. In particular, the robust accuracy of our approach was consistently higher for various attacks such as PGD, FGSM, and BIM and more powerful adversaries. By visualizing the attention map, we further confirmed that the attention module extracts features of the correct class even for adversarial examples.