• Journal of the Korea Fashion and Costume Design Association
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• v.23 no.2
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• pp.67-78
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• 2021

This study aims to analyze how students' design work functions affect consumer attitudes and purchase intentions toward clothes designed by students, while exploring the moderating effect of price sensitivity in such a relationship. Data was acquired from 351 responses of an online questionnaire (www.sojump.com). A two-step approach was employed to analyze our hypotheses using structural equation modeling (SEM) in SPSS 22.0 and AMOS 22.0 statistical packages. First, significant empirical evidence was secured regarding the effects of design functions (assurance, fashion, camouflage, individuality, and comfort) on consumer attitudes toward clothes, which can lead to purchasing intention. Fashion, individuality, and comfort functions can enhance consumer attitude significantly, but assurance and camouflage have no significant influence. Among the functions, comfort has the greatest effect on consumer attitudes, indicating that when students market works as commodities, comfort should be highlighted in their designs. In this way, such products can draw the interest of many consumers. Second, empirical evidence showed that price sensitivity negatively moderates the association between attitude and purchase intention. Thus, design courses should be careful when setting student works' prices given consumer sensitivity. The optimization of the student works' cost structure can help minimize price sensitivity. Overall, the findings and their implications can serve as a basis for the commercial application of design curriculum works and provide feasible support for developing student design curriculum in the future.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.66.1-66.1
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• 2021

Extinction curves observed toward individual Active Galactic Nuclei (AGN) usually show a steep rise toward Far-Ultraviolet (FUV) wavelengths and can be described by the Small Magellanic Cloud (SMC)-like dust model. This feature suggests the dominance of small dust grains of size a < 0.1 ㎛ in the local environment of AGN, but the origin of such small grains is unclear. In this paper, we aim to explain this observed feature by applying the RAdiative Torque Disruption (RATD) to model the extinction of AGN radiation from FUV to Mid-Infrared (MIR) wavelengths. We find that in the intense radiation field of AGN, large composite grains of size a > 0.1 ㎛ are significantly disrupted to smaller sizes by RATD up to dRATD > 100 pc in the polar direction and dRATD ~ 10 pc in the torus region. Consequently, optical-MIR extinction decreases, whereas FUV-near-Ultraviolet extinction increases, producing a steep far-UV rise extinction curve. The resulting total-to selective visual extinction ratio thus significantly drops to RV < 3.1 with decreasing distances to AGN center due to the enhancement of small grains. The dependence of RV with the efficiency of RATD will help us to study the dust properties in the AGN environment via photometric observations. In addition, we suggest that the combination of the strength between RATD and other dust destruction mechanisms that are responsible for destroying very small grains of a <0.05 ㎛ is the key for explaining the dichotomy observed "SMC" and "gray" extinction curve toward many AGN.

• Journal of Fashion Business
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• v.27 no.5
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• pp.78-92
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• 2023

Fashion trends are actively disseminated through social media, which influences both their propagation and consumption. This study explored how users perceive subversive basic fashion in social media videos, by examining the associated concepts and characteristics. In addition, the factors contributing to the style's social media dissemination were identified and its distinctive features were analyzed. Through text mining analysis, 80 keywords were selected for semantic network and CONCOR analysis. TF-IDF and N-gram results indicate that subversive basic fashion involves transformative design techniques such as cutting or layering garments, emphasizing the body with thin fabrics, and creating bold visual effects. Topic modeling suggests that this fashion forms a subculture that resists mainstream norms, seeking individuality by creatively transforming the existing garments. CONCOR analysis categorized the style into six groups: forward-thinking unconventional fashion, bold and unique style, creative reworking, item utilization and combination, pursuit of easy and convenient fashion, and contemporary sensibility. Consumer actions, linked to social media, were shown to involve easily transforming and pursuing personalized styles. Furthermore, creating new styles through the existing clothing is seen as an economic and creative activity that fosters network formation and interaction. This study is significant as it addresses language expression limitations and subjectivity issues in fashion image analysis, revealing factors contributing to content reproduction through user-perceived design concepts and social media-conveyed fashion characteristics.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.438-455
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• 2024

Recently, many unsupervised learning-based models have emerged for Just Noticeable Difference (JND) estimation, demonstrating remarkable improvements in accuracy. However, these models suffer from a significant drawback is that their heavy reliance on handcrafted priors for guidance. This restricts the information for estimating JND simply extracted from regions that are highly related to handcrafted priors, while information from the rest of the regions is disregarded, thus limiting the accuracy of JND estimation. To address such issue, on the one hand, we extract the information for estimating JND in an Adversarial Complementary Learning (ACoL) way and propose an ACoL-JND network to estimate the JND by comprehensively considering the handcrafted priors-related regions and non-related regions. On the other hand, to make the handcrafted priors richer, we take two additional priors that are highly related to JND modeling into account, i.e., Patterned Masking (PM) and Contrast Masking (CM). Experimental results demonstrate that our proposed model outperforms the existing JND models and achieves state-of-the-art performance in both subjective viewing tests and objective metrics assessments.

• Journal of the Korea Computer Graphics Society
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• v.14 no.4
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• pp.7-18
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• 2008

To realistically produce fluids such as smoke for the visual effects in the films or animations, we need two main processes: a physics-based modeling of smoke and a rendering of smoke simulation data, based on light transport theory. In the computer graphics community, the physics-based fluids simulation is generally adopted for smoke modeling. Recently, the interest of the particle-based Lagrangian simulation methods is increasing due to the advantages at simulation time, instead of the grid-based Eulerian simulation methods which was widely used. As a result, because the smoke rendering technique depends heavily on the modeling method, the research for rendering of the particle-based smoke data still remains challenging while the research for rendering of the grid-based smoke data is actively in progress. This paper focuses on realistic rendering technique for the smoke particles produced by Lagrangian simulation method. This paper introduces a technique which is called particle map, that is the expansion and modification of photon mapping technique for the particle data. And then, this paper suggests the novel particle map technique and shows the differences and improvements, compared to previous work. In addition, this paper presents irradiance map technique which is the pre-calculation of the multiple scattering term in the volume rendering equation to enhance efficiency at rendering time.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.22
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• pp.9731-9737
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• 2014

Large scale secular registry or surveillance systems have been accumulating vast data that allow mathematical modeling of cancer incidence and mortality rates. Most contemporary models in this regard use time series and APC (age-period-cohort) methods and focus primarily on predicting or analyzing cancer epidemiology with little attention being paid to implications for designing cancer registry, surveillance or evaluation initiatives. This research models age-specific cancer incidence rates using logistic growth equations and explores their performance under different scenarios of data completeness in the hope of deriving clues for reshaping relevant data collection. The study used China Cancer Registry Report 2012 as the data source. It employed 3-parameter logistic growth equations and modeled the age-specific incidence rates of all and the top 10 cancers presented in the registry report. The study performed 3 types of modeling, namely full age-span by fitting, multiple 5-year-segment fitting and single-segment fitting. Measurement of model performance adopted adjusted goodness of fit that combines sum of squred residuals and relative errors. Both model simulation and performance evalation utilized self-developed algorithms programed using C# languade and MS Visual Studio 2008. For models built upon full age-span data, predicted age-specific cancer incidence rates fitted very well with observed values for most (except cervical and breast) cancers with estimated goodness of fit (Rs) being over 0.96. When a given cancer is concerned, the R valuae of the logistic growth model derived using observed data from urban residents was greater than or at least equal to that of the same model built on data from rural people. For models based on multiple-5-year-segment data, the Rs remained fairly high (over 0.89) until 3-fourths of the data segments were excluded. For models using a fixed length single-segment of observed data, the older the age covered by the corresponding data segment, the higher the resulting Rs. Logistic growth models describe age-specific incidence rates perfectly for most cancers and may be used to inform data collection for purposes of monitoring and analyzing cancer epidemic. Helped by appropriate logistic growth equations, the work vomume of contemporary data collection, e.g., cancer registry and surveilance systems, may be reduced substantially.

• The KIPS Transactions:PartC
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• v.18C no.4
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• pp.265-278
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• 2011

The objective of this paper is to analyze and characterize end-to-end packet statistics after modeling and simulation of WiFi (IEEE 802.11g) and WiMAX (IEEE 802.16e) of a virtual wireless network using OPNET Modeler Wireless Suite. Wireless internal and external network simulators such as Remcom's Wireless InSite Real Time (RT) module, WinProp: W-LAN/Fixed WiMAX/Mobile WiMAX, and SMI system, are designed to consider data transfer rate based on wireless propagation signal strength. However, we approached our research in a different perspective without support for characteristic of these wireless network simulators. That is, we will discuss the purpose of a visual analysis for these packets, how to receive each point packets (e.g., wireless user, base station or access point, and http server) through end-to-end virtual network modeling based on integrated wired and wireless network without wireless propagation signal strength. Measuring packet statistics is important in QoS metric analysis among wireless network performance metrics. Clear packet statistics is an especially essential metric in guaranteeing QoS for WiMAX users. We have found some interesting results through modeling and simulation for virtual wireless network using OPNET Modeler Wireless Suite. We are also able to analyze multi-view efficiency through experiment/observation result.

• Korean Journal of Construction Engineering and Management
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• v.20 no.1
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• pp.114-123
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• 2019

Tracking ICT applications on construction business processes is critical to the success of ICT-applied construction projects. Existing IDEF0 is a representative modeling method for visualizing and analyzing business processes. It defines a construction production process into a visual information model, hence, encouraging the project participant to understand the activities, their deliverable, and control flow of the process. However, IDEF0 dose not lend itself to ICT-applied construction processes, because it does not provide a mean to define how, in what order, by which each and every activity that ICT applied implements. This paper presents a new business modeling method that improves the traceability of ICT application (IAMB: ICT Application tracking Model for Business process) for construction management. The IAMB contributes to handle the sophisticated features of construction management processes to which ICT are applied. The method categorizes the process into three types: management, construction, and information exchange. The validity of IAMB was confirmed by analyzing the performance when it is used for tracking each modeling step of lift reservation process which making use of ICT. The test case provides an admissible evidence that the method encourage to define who, what, how, which order, and by which ICT tools the construction process exchanges production information.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.57-65
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• 2021

A hyper-connected intelligence information society is emerging that creates new value by converging IoT, AI, and Bigdata, which are new technologies of the fourth industrial revolution, in all industrial fields. Everything is connected to the network and data is exploding, and artificial intelligence can learn on its own and even intellectual judgment functions are possible. In particular, the Internet of Things provides a new communication environment that can be connected to anything, anytime, anywhere, enabling super-connections where everything is connected. Artificial intelligence technology is implemented so that computers can execute human perceptions, learning, reasoning, and natural language processing. Artificial intelligence is developing advanced technologies such as machine learning, deep learning, natural language processing, voice recognition, and visual recognition, and includes software, machine learning, and cloud technologies specialized in various applications such as safety, medical, defense, finance, and welfare. Through this, it is utilized in various fields throughout the industry to provide human convenience and new values. However, on the contrary, it is time to respond as intelligent and sophisticated cyber threats are increasing and accompanied by potential adverse functions such as securing the technical safety of new technologies. In this paper, we propose a new data modeling method to enable IoT integrated security control by utilizing artificial intelligence technology as a way to solve these adverse functions.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.2
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• pp.249-256
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• 2022

Various studies are being actively conducted to show that the real-time visualization technology that combines BIM (Building Information Modeling) and AR (Augmented Reality) helps to increase construction management decision-making and processing efficiency. However, when large-capacity BIM data is projected into AR, there are various limitations such as data transmission and connection problems and the image cut-off issue. To improve the high efficiency of visualizing, a mesh optimization algorithm based on the k-nearest neighbors (KNN) classification framework to reconstruct BIM data is proposed in place of existing mesh optimization methods that are complicated and cannot adequately handle meshes with numerous boundaries of the 3D models. In the proposed algorithm, our target BIM model is optimized with the Unity C# code based on triangle centroid concepts and classified using the KNN. As a result, the algorithm can check the number of mesh vertices and triangles before and after optimization of the entire model and each structure. In addition, it is able to optimize the mesh vertices of the original model by approximately 56 % and the triangles by about 42 %. Moreover, compared to the original model, the optimized model shows no visual differences in the model elements and information, meaning that high-performance visualization can be expected when using AR devices.