• The Journal of the Korea Contents Association
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• v.21 no.8
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• pp.60-68
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• 2021

The purpose of this paper is to examine the formal characteristics of , which has been screened on Netflix since 2018. This film can be considered an interactive narrative because it gives viewers the opportunity to select their own narrative forks which lead to various endings. However, it also limits viewers' freedom of interactions in many ways, resulting in the pessimistic narrative world of series. In this contradictory situation, the conflict between the user's selectability and the narrator's authoriality emerges. And this collision gives rise to a complex form in which nonlinear interactive and linear narrative forms blend together. It can be understood as a form of self-reflection, such as forms of the metalepsis and breaking the fourth wall. In this paper, this particular form will be regarded as a sort of reflexive intermediality, i. e. the form for media reflexion.

• Geophysics and Geophysical Exploration
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• v.24 no.4
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• pp.164-170
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• 2021

Frequency-domain and time-domain induced polarization methods can provide spectral information about subsurface media. Analysis of spectral characteristics has been studied mainly in the frequency-domain, however, time-domain induced polarization research has recently become popular. In this study, assuming a homogeneous half-space model, an inversion method was developed to extract Cole-Cole parameters from the measured secondary potential or electrical resistivity. Since the Cole-Cole parameters of chargeability, time constant, and frequency index are not independent of each other, various problems, such as slow convergence rate, initial model problem, local minimum problem, and divergence, frequently occur when conventional nonlinear inversion is applied. In this study, we developed an effective inversion method using the initial model close to the true model by introducing a grid search method. Finally, the validity of the developed inversion method was verified using inversion experiments.

• Nuclear Engineering and Technology
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• v.54 no.10
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• pp.3841-3848
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• 2022

The effect of modifiers on the optical features and radiation defying ability of the Eu³⁺ ions doped multi constituent glasses was examined. XRD has established the amorphous nature of the specimen. The presence of various functional/fundamental groups in the present glasses was analyzed through FTIR spectra. The physical, structural and elastic traits of the glasses were explored. The variation in the structural compactness of the glass structure according to the incorporated modifier was enlightened to describe their suitability for a better shielding media. For the examined glasses, the metallization criterion value varied in the range 0.613-0.692, indicating the non-metallic character of the glasses with possible nonlinear optical applications. The computed elastic moduli expose the Li-containing glass (BTLi:Eu) to be tightly packed and rigid, which is a requirement for a better shielding channel. Furthermore, the optical bandgap and the Urbach energy values are calculated based on the optical absorption spectra. The evaluated bonding parameters revealed the nature of the fabricated glasses covalent. In addition, we investigated the radiation attenuation attributes of the prepared Eu³⁺ ions doped multi constituent glasses using Phy-X software. We determined the linear attenuation coefficient (LAC) and reported the influence of the five oxides Li₂O₃, CaO, BaO, SrO, and ZnO on the LAC values. The LAC varied between 0.433 and 0.549 cm^-1 at 0.284 MeV. The 39B₂O₃-25TeO₂-15Li₂O₃-10Na₂O-10K₂O-1Eu₂O₃ glass has a much smaller LAC than the other glasses.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.145-147
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• 2020

As deep learning technology advances, there have been many attempts to improve video codecs such as High-Efficiency-Video-Coding (HEVC) using deep learning technology. One of the most researched approaches is improving filters inside codecs through image restoration researches. In this paper, we propose a method 01 replacing the sample adaptive offset (SAO) filtering with a deep neural network. The proposed method uses the deep neural network to find the optimal offset value. The proposed network consists of two subnetworks to find the offset value and its type of the signal, which can restore nonlinear and complex type of error. Experimental results show that the performance is better than the conventional HEVC in low delay P and random access mode.

• Journal of Broadcast Engineering
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• v.1 no.2
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• pp.176-187
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• 1996

This paper deals with the accurate estimation of 3- D pose (position and orientation) of a moving object with reference to the world frame (or robot base frame), based on a sequence of stereo images taken by cameras mounted on the end - effector of a robot manipulator. This work is an extension of the previous work[1]. Emphasis is given to the 3-D pose estimation relative to the world (or robot base) frame under the presence of not only the measurement noise in 2 - D images[ 1] but also the camera position errors due to the random noise involved in joint angles of a robot manipulator. To this end, a new set of discrete linear Kalman filter equations is derived, based on the following: 1) the orientation error of the object frame due to measurement noise in 2 - D images is modeled with reference to the camera frame by analyzing the noise propagation through 3- D reconstruction; 2) an extended Jacobian matrix is formulated by combining the result of 1) and the orientation error of the end-effector frame due to joint angle errors through robot differential kinematics; and 3) the rotational motion of an object, which is nonlinear in nature, is linearized based on quaternions. Motion parameters are computed from the estimated quaternions based on the iterated least-squares method. Simulation results show the significant reduction of estimation errors and also demonstrate an accurate convergence of the actual motion parameters to the true values.

• Journal of Broadcast Engineering
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• v.16 no.6
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• pp.986-995
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• 2011

In this paper, we propose a noise whitening decision feedback equalizer for single carrier frequency division multiple access (SC-FDMA) receivers. SC-FDMA has the same advantage as that of orthogonal frequency division multiple access (OFDMA) in which the multipath effect can be removed easily, and also solves the problem of high peak to average power ratio (PAPR) which is the main drawback of OFDMA. Although SC-FDMA is a single carrier transmission scheme, a simple frequency domain linear equalizer (FD-LE) can be implemented as in OFDMA, which can dramatically reduce the equalizer complexity. Moreover, some residual intersymbol interference in the output of the FD-LE can be further removed by an additional nonlinear decision feedback equalizer (DFE) in time domain, because the time domain signal is a digitally modulated symbol. In the conventional DFE, however, the noise is not white at the input of the decision device and correspondingly the decision is not optimum. In this paper, we propose an improved DFE scheme for SC-FDMA systems where a linear noise whitening filter is inserted before the decision device of the conventional DFE scheme. Through computer simulations, we compare the bit error rate performance of the proposed DFE scheme with the conventional equalizers.

• Journal of Environmental Science International
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• v.19 no.5
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• pp.549-563
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• 2010

The environmental behaviors of polycyclic aromatic hydrocarbons (PAHs) are mainly governed by their solubility and partitioning properties on soil media in a subsurface system. In surfactant-enhanced remediation (SER) systems, surfactant plays a critical role in remediation. In this study, sorptive behaviors and partitioning of naphthalene in soils in the presence of surfactants were investigated. Silica and kaolin with low organic carbon contents and a natural soil with relatively higher organic carbon content were used as model sorbents. A nonionic surfactant, Triton X-100, was used to enhance dissolution of naphthalene. Sorption kinetics of naphthalene onto silica, kaolin and natural soil were investigated and analyzed using several kinetic models. The two compartment first-order kinetic model (TCFOKM) was fitted better than the other models. From the results of TCFOKM, the fast sorption coefficient of naphthalene ($k_1$) was in the order of silica > kaolin > natural soil, whereas the slow sorbing fraction ($k_2$) was in the reverse order. Sorption isotherms of naphthalene were linear with organic carbon content ($f_{oc}$) in soils, while those of Triton X-100 were nonlinear and correlated with CEC and BET surface area. Sorption of Triton X-100 was higher than that of naphthalene in all soils. The effectiveness of a SER system depends on the distribution coefficient ($K_D$) of naphthalene between mobile and immobile phases. In surfactant-sorbed soils, naphthalene was adsorbed onto the soil surface and also partitioned onto the sorbed surfactant. The partition coefficient ($K_D$) of naphthalene increased with surfactant concentration. However, the $K_D$ decreased as the surfactant concentration increased above CMC in all soils. This indicates that naphthalene was partitioned competitively onto both sorbed surfactants (immobile phase) and micelles (mobile phase). For the mineral soils such as silica and kaolin, naphthalene removal by mobile phase would be better than that by immobile phase because the distribution of naphthalene onto the micelles ($K_{mic}$) increased with the nonionic surfactant concentration (Triton X-100). For the natural soil with relatively higher organic carbon content, however, the naphthalene removal by immobile phase would be better than that by mobile phase, because a high amount of Triton X-100 could be sorbed onto the natural soil and the sorbed surfactant also could sorb the relatively higher amount of naphthalene.

• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.378-390
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• 2022

With the recent development of deep composite multiplication neural network learning, deep learning techniques applied to single-image super-resolution have shown good results, and the strong expression ability of deep networks has enabled complex nonlinear mapping between low-resolution and high-resolution images. However, there are limitations in applying it to real-time or low-power devices with increasing parameters and computational amounts due to excessive use of composite multiplication neural networks. This paper uses blocks that extract hierarchical characteristics little by little using information distillation and suggests the Recursive Distillation Super Resolution Network (RDSRN), a lightweight network that improves performance by making more accurate high frequency components through high frequency residual purification blocks. It was confirmed that the proposed network restores images of similar quality compared to RDN, restores images 3.5 times faster with about 32 times fewer parameters and about 10 times less computation, and produces 0.16 dB better performance with about 2.2 times less parameters and 1.8 times faster processing time than the existing lightweight network CARN.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.21-33
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• 2022

Recently, the recommender system has been widely used in various fields such as movies, music, online shopping, and social media, and in the meantime, the recommender model has been developed from correlation analysis through the Apriori model, which can be said to be the first-generation model in the recommender system field. In 2005, many models have been proposed, including deep learning-based models, which are receiving a lot of attention within the recommender model. The recommender model can be classified into a collaborative filtering method, a content-based method, and a hybrid method that uses these two methods integrally. However, these basic methods are gradually losing their status as methodologies in the field as they fail to adapt to internal and external changing factors such as the rapidly changing user-item interaction and the development of big data. On the other hand, the importance of deep learning methodologies in recommender systems is increasing because of its advantages such as nonlinear transformation, representation learning, sequence modeling, and flexibility. In this paper, among deep learning methodologies, RNN, CNN, and GAN-based models suitable for sequential modeling that can accurately and flexibly analyze user-item interactions are classified, compared, and analyzed.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.3
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• pp.371-381
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• 2017

This paper presents a methodology for creating abstract animation based on the human movement theories originating from the work of dance theorist Rudolf von Laban. Laban Movement Analysis is a method and language for describing, visualizing, interpreting and documenting all varieties of human movement, and Choreutics is based on universal patterns of nature and of human as part of a universal design. Laban defines the space of movements in a profoundly dualistic way. Outwardly, his objective and scientific definitions provide a concrete base for generating human movements in computer graphics in terms of geometric and motion primitives such as points, lines, planes, polygons, linear and nonlinear movements. On the other hand, he also offers a system for understanding the subtle characteristics about the way a movement is dynamically done with respect to inner intention. Laban's interpretations of human motion can be utilized potentially in plastic arts and computer arts. Our work was inspired by those physical and psychological analyses and computer algorithms have been developed for creating abstract animation. We presented our computer animation works entitled "Choreography" in the exhibitions: a special section in "2015 Craft Trend Fair" and "Make Your Movement" held in the Korean Cultural Centre in UK, 2016. In this paper, we describe our ideas and methods for creating abstract object movements based on the Laban's motion representations.