• Journal of the Korean Society of Industry Convergence
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• v.26 no.3
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• pp.471-481
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• 2023

Digital twin is considered as a key technology of industry 4.0, thus being essential for the future of industrial production. Despite the significance, a systematic analysis of its technological landscape is lacking. This study aims to investigate the technological development trends and newly emerging technological convergence opportunities in the domain of digital twin by exploiting patent information derived from U SPTO. For this purpose, this study visualized and predicted the convergence dynamics among patent classification codes by adopting patent co-classification analysis and link prediction approach. The findings show that the number of digital twin-related patent applications has increased significantly since 2018. The CPC code G06F showed the highest eigenvector centrality, while G05B was characterized by highest betweenness centrality. According to the predictive model, 41 novel links were revealed, acting as potential technology convergence opportunities. These links were then categorized into 11 different domains. The most dominant category was "digital data processing and artificial intelligence", which could play a foundational role in the diffusion of digital twin technology. The presence of digital twin technology is dominant in manufacturing, but its applications are expected to expand, including "climate change", "healthcare" and "aerospace engineering". The derived insights can support R&D managers and policy makers in formulating R&D strategies and directing future R&D investment decisions.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.162-162
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• 2022

Stormwater management is an essential component of land-use planning and development. Due to the additional challenges posed by climate change and urbanization, various stormwater management schemes have been developed to limit flood damages and ease water quality concerns. Nature-based solutions (NBS) are increasingly used as cost-effective measures to manage stormwater runoff from various land uses. Specifically, constructed wetlands were already considered as socially acceptable green stormwater infrastructures that are widely used in different countries. There is a large collection of published literature regarding the effectiveness or efficiency of constructed wetlands in treating stormwater runoff; however, metadata analyses using bibliographic information are very limited or seldomly explored. This study was conducted to determine the trends of publication regarding stormwater treatment wetlands using a bibliometric analysis approach. Moreover, the research productivity of various countries, authors, and institutions were also identified in the study. The Web of Science (WoS) database was utilized to retrieve bibliographic information. The keywords ("constructed wetland*" OR "treatment wetland*" OR "engineered wetland*" OR "artificial wetland*") AND ("stormwater*" or "storm water*") were used to retrieve pertinent information on stormwater treatment wetlands-related publication from 1990 up to 2021. The network map of keyword co-occurrence map was generated through the VOSviewer software and the contingency matrices were obtained using the Cortext platform (www.cortext.net). The results obtained from this inquiry revealed the areas of research that have been adequately explored by past studies. Furthermore, the extensive collection of published scientific literature enabled the identification of existing knowledge gaps in the field of stormwater treatment wetlands.

• Asia pacific journal of information systems
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• v.34 no.1
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• pp.150-190
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• 2024

This article proposes a framework to elucidate the structural dynamics of the generative AI ecosystem. It also outlines the practical application of this proposed framework through illustrative policies, with a specific emphasis on the development of the Korean generative AI ecosystem and its implications of platform strategies at AI platform-squared. We propose a comprehensive classification scheme within generative AI ecosystems, including app builders, technology partners, app stores, foundational AI models operating as operating systems, cloud services, and chip manufacturers. The market competitiveness for both app builders and technology partners will be highly contingent on their ability to effectively navigate the customer decision journey (CDJ) while offering localized services that fill the gaps left by foundational models. The strategically important platform of platforms in the generative AI ecosystem (i.e., AI platform-squared) is constituted by app stores, foundational AIs as operating systems, and cloud services. A few companies, primarily in the U.S. and China, are projected to dominate this AI platform squared, and consequently, they are likely to become the primary targets of non-market strategies by diverse governments and communities. Korea still has chances in AI platform-squared, but the window of opportunities is narrowing. A cautious approach is necessary when considering potential regulations for domestic large AI models and platforms. Hastily importing foreign regulatory frameworks and non-market strategies, such as those from Europe, could overlook the essential hierarchical structure that our framework underscores. Our study suggests a clear strategic pathway for Korea to emerge as a generative AI powerhouse. As one of the few countries boasting significant companies within the foundational AI models (which need to collaborate with each other) and chip manufacturing sectors, it is vital for Korea to leverage its unique position and strategically penetrate the platform-squared segment-app stores, operating systems, and cloud services. Given the potential network effects and winner-takes-all dynamics in AI platform-squared, this endeavor is of immediate urgency. To facilitate this transition, it is recommended that the government implement promotional policies that strategically nurture these AI platform-squared, rather than restrict them through regulations and stakeholder pressures.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.6C
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• pp.267-274
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• 2012

The conventional approach to evaluate the contaminant transport in soils adopts the macro-scale implementation while the pore configuration and network is a dominant factor to determine the fate of contaminant. However, the observation of fate and transport at pore scale may not be readily approachable because of the computational expenses to solve Navier-Stokes equation. We herein present the 2D Lattice-Boltzmann method that enables to assess the local fluid velocity and density efficiently for the case of single phase and multi-components. The solute fate spatio-temperal space is explicitly determined by the advection of fluid flow. Two different types of idealized pore space provides the path of fluid. Also, solute transport, the velocity field and average concentration of solute are computed in steady state. Results show that the pore geometry such as tortuosity mainly affect the solute fate. It highlights the significance of the pore configuration and shape in granular soils and rock discontinuity in spite of the equivalent porosity.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.6
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• pp.792-798
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• 2007

In this paper, we propose a hierarchical fuzzy-rough classification method based on statistical information for maximizing the performance of pattern classification and reducing the number of rules without learning approaches such as neural network, genetic algorithm. In the proposed method, statistical information is used for extracting the partition intervals of antecedent fuzzy sets at each layer on hierarchical fuzzy-rough classification systems and rough sets are used for minimizing the number of fuzzy if-then rules which are associated with the partition intervals extracted by statistical information. To show the effectiveness of the proposed method, we compared the classification results(e.g. the classification accuracy and the number of rules) of the proposed with those of the conventional methods on the Fisher's IRIS data. From the experimental results, we can confirm the fact that the proposed method considers only statistical information of the given data is similar to the classification performance of the conventional methods.

• Applied Chemistry for Engineering
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• v.28 no.4
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• pp.375-382
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• 2017

In the present review, we provide an overview of the research trend of anodic $TiO_2$ nanostructures. To date, most anodic $TiO_2$ nanostructure formation has focused on the fluoride ion electrolyte system to form nanotube layers. Recently, a novel approach that describes the formation of thick, self-organized $TiO_2$ nanostructures was reported. These layers can be prepared on Ti metal by anodization in a hot organic/$K_2HPO_4$ electrolyte. This nanostructure consists of a strongly interlinked network of nanosized $TiO_2$, and thus provides a considerably higher specific surface area than that of using anodic $TiO_2$ nanotubes. This review describes the formation mechanism and novel properties of the new nanostructures, and introduces potential applications.

• The Journal of Internal Korean Medicine
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• v.22 no.4
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• pp.639-645
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• 2001

Angiogenesis is a fundamental process in reproduction and wound healing. Under these condition, neovascularization is tightly regulated. Unregulated angiogenesis may lead to several angiogenic diseases, and is thought to be indispensible for solid tumor growth and metastsis. The construction of new vascular network is a multistep cascade involving basement membrane degradation, endothelial cell proliferation, endothelial cell migration, and tube formation. Newly reported anti-angiogenic agents in oriental medical field have targeted both specific and multistep stages in the angiogenic process. From recent approach in oriental medical field with several herb medicines including activating blood flow and removing blood stasis medicine(活血化瘀藥), it may be possible in the future to develope specific anti-angiogenic agents that offer a less toxic potential therapy for cancer and angiogenic disease.

• International Journal of Contents
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• v.13 no.2
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• pp.29-34
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• 2017

Cloud computing is becoming an effective and efficient way of computing resources and computing service integration. Through centralized management of resources and services, cloud computing delivers hosted services over the internet, such that access to shared hardware, software, applications, information, and all resources is elastically provided to the consumer on-demand. The main enabling technology for cloud computing is virtualization. Virtualization software creates a temporarily simulated or extended version of computing and network resources. The objectives of virtualization are as follows: first, to fully utilize the shared resources by applying partitioning and time-sharing; second, to centralize resource management; third, to enhance cloud data center agility and provide the required scalability and elasticity for on-demand capabilities; fourth, to improve testing and running software diagnostics on different operating platforms; and fifth, to improve the portability of applications and workload migration capabilities. One of the key features of cloud computing is elasticity. It enables users to create and remove virtual computing resources dynamically according to the changing demand, but it is not easy to make a decision regarding the right amount of resources. Indeed, proper provisioning of the resources to applications is an important issue in IaaS cloud computing. Most web applications encounter large and fluctuating task requests. In predictable situations, the resources can be provisioned in advance through capacity planning techniques. But in case of unplanned and spike requests, it would be desirable to automatically scale the resources, called auto-scaling, which adjusts the resources allocated to applications based on its need at any given time. This would free the user from the burden of deciding how many resources are necessary each time. In this work, we propose an analytical and efficient VM-level scaling scheme by modeling each VM in a data center as an M/M/1 processor sharing queue. Our proposed VM-level scaling scheme is validated via a numerical experiment.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.9 s.351
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• pp.60-68
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• 2006

Using the wireless communication among unacquainted vehicles, an intelligent vehicle safety system can be constructed to exchange vehicle safety-related information, such as urgency stop, traffic accident and road obstacles. In the majority of vehicle safety applications, vehicle safety messages are propagated in the form of broadcast. However, this approach causes some effectiveness and performance problems with massive radio collision, multi-hop propagation. This paper presents a priority based relay node selection method for propagating vehicle safety messages of traffic accident protection system. With this method, vehicle safety messages are relayed by a node that locates in proper distance out of the nodes that are included in the radio transmission range. By decreasing the number of duplicated messages, the packet overhead is lessened while the communication performance is raised. The proposed method was proven to be better than other schemes through network simulations.

• Bulletin of the Korean Chemical Society
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• v.32 no.12
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• pp.4397-4402
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• 2011

The NF-${\kappa}B$ system of transcription factors plays a crucial role in inflammatory diseases, making it an important drug target. We combined quantitative structure activity relationships for predicting the activity of new compounds and quantitative dynamic models for the NF-${\kappa}B$ network with intracellular concentration models. GFA-MLR QSAR analysis was employed to determine the optimal QSAR equation. To validate the predictability of the $IKK{\beta}$ QSAR model for an external set of inhibitors, a set of ordinary differential equations and mass action kinetics were used for modeling the NF-${\kappa}B$ dynamic system. The reaction parameters were obtained from previously reported research. In the IKKb QSAR model, good cross-validated $q^2$ (0.782) and conventional $r^2$ (0.808) values demonstrated the correlation between the descriptors and each of their activities and reliably predicted the $IKK{\beta}$ activities. Using a developed simulation model of the NF-${\kappa}B$ signaling pathway, we demonstrated differences in $I{\kappa}B$ mRNA expression between normal and different inhibitory states. When the inhibition efficiency increased, inhibitor 1 (PS-1145) led to long-term oscillations. The combined computational modeling and NF-${\kappa}B$ dynamic simulations can be used to understand the inhibition mechanisms and thereby result in the design of mechanism-based inhibitors.