• Clean Technology
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• v.28 no.2
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• pp.138-146
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• 2022

Electricity has become a factor that dramatically affects the market economy. The day-ahead system marginal price determines electricity prices, and system marginal price forecasting is critical in maintaining energy management systems. There have been several studies using mathematics and machine learning models to forecast the system marginal price, but few studies have been conducted to develop, compare, and analyze various machine learning and deep learning models based on a data-driven framework. Therefore, in this study, different machine learning algorithms (i.e., autoregressive-based models such as the autoregressive integrated moving average model) and deep learning networks (i.e., recurrent neural network-based models such as the long short-term memory and gated recurrent unit model) are considered and integrated evaluation metrics including a forecasting test and information criteria are proposed to discern the optimal forecasting model. A case study of South Korea using long-term time-series system marginal price data from 2016 to 2021 was applied to the developed framework. The results of the study indicate that the autoregressive integrated moving average model (R-squared score: 0.97) and the gated recurrent unit model (R-squared score: 0.94) are appropriate for system marginal price forecasting. This study is expected to contribute significantly to energy management systems and the suggested framework can be explicitly applied for renewable energy networks.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.9
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• pp.1199-1205
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• 2021

In the era of big data, not only structured data well organized in databases, but also the Internet, social network services, it is very important to effectively analyze unstructured big data such as web documents, e-mails, and social data generated in real time in mobile environment. Big data analysis is the process of creating new value by discovering meaningful new correlations, patterns, and trends in big data stored in data storage. We intend to summarize and visualize the analysis results through frequency analysis of unstructured article data using R language, a big data analysis tool. The data used in this study was analyzed for total 104 papers in the Mon-May 2021 among the journals of the Korea Institute of Information and Communication Engineering. In the final analysis results, the most frequently mentioned keyword was "Data", which ranked first 1,538 times. Therefore, based on the results of the analysis, the limitations of the study and theoretical implications are suggested.

• IMMUNE NETWORK
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• v.22 no.6
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• pp.51.1-51.20
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• 2022

Trypanosoma cruzi, the etiological agent of Chagas disease, is an intracellular protozoan parasite, which is now present in most industrialized countries. About 40% of T. cruzi infected individuals will develop severe, incurable cardiovascular, gastrointestinal, or neurological disorders. The molecular mechanisms by which T. cruzi induces cardiopathogenesis remain to be determined. Previous studies showed that increased IL-6 expression in T. cruzi patients was associated with disease severity. IL-6 signaling was suggested to induce pro-inflammatory and pro-fibrotic responses, however, the role of this pathway during early infection remains to be elucidated. We reported that T. cruzi can dysregulate the expression of host PIWI-interacting RNAs (piRNAs) during early infection. Here, we aim to evaluate the dysregulation of IL-6 signaling and the piRNAs computationally predicted to target IL-6 molecules during early T. cruzi infection of primary human cardiac fibroblasts (PHCF). Using in silico analysis, we predict that piR_004506, piR_001356, and piR_017716 target IL6 and SOCS3 genes, respectively. We validated the piRNAs and target gene expression in T. cruzi challenged PHCF. Secreted IL-6, soluble gp-130, and sIL-6R in condition media were measured using a cytokine array and western blot analysis was used to measure pathway activation. We created a network of piRNAs, target genes, and genes within one degree of biological interaction. Our analysis revealed an inverse relationship between piRNA expression and the target transcripts during early infection, denoting the IL-6 pathway targeting piRNAs can be developed as potential therapeutics to mitigate T. cruzi cardiomyopathies.

• Journal of Technology Innovation
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• v.21 no.1
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• pp.279-301
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• 2013

This study demonstrates how social network analysis can be used for identifying potential buyers in technology marketing; in such, the methodology and empirical results are proposed. First of all, we derived the three most important 'seed' keywords from 'technology description' sections. The technologies are generated by various types of R&D activities organized by South Korea's public research institutes in the fundamental science fields. Second, some 3, 000 words were collected from websites related to the three 'seed' keywords. Next, three network matrices (i.e., one matrix per seed keyword) were constructed. To explore the technology network structure, each network is analyzed by degree centrality and Euclidean distance. The network analysis suggests 100 potentially demanding companies and identifies seven common companies after comparing results derived from each network. The usefulness of the result is verified by investigating the business area of the firm's homepages. Finally, five out of seven firms were proven to have strong relevance to the target technology. In terms of social network analysis, this study expands its application scope of methodology by combining semantic network analysis and the technology marketing method. From a practical perspective, the empirical study suggests the illustrative framework for exploiting prospective demanding companies on the web, raising possibilities of technology commercialization in the basic research fields. Future research is planned to examine how the efficiency of process and accuracy of result is increased.

• Journal of Ginseng Research
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• v.44 no.5
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• pp.690-696
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• 2020

Background: As the main metabolites of ginsenosides, 20(S, R)-protopanaxadiol [PPD(S, R)] and 20(S, R)-protopanaxatriol [PPT(S, R)] are the structural basis response to a series of pharmacological effects of their parent components. Although the estrogenicity of several ginsenosides has been confirmed, however, the underlying mechanisms of their estrogenic effects are still largely unclear. In this work, PPD(S, R) and PPT(S, R) were assessed for their ability to bind and activate human estrogen receptor α (hERα) by a combination of in vitro and in silico analysis. Methods: The recombinant hERα ligand-binding domain (hERα-LBD) was expressed in E. coli strain. The direct binding interactions of ginsenosides with hERα-LBD and their ERα agonistic potency were investigated by fluorescence polarization and reporter gene assays, respectively. Then, molecular dynamics simulations were carried out to simulate the binding modes between ginsenosides and hERα-LBD to reveal the structural basis for their agonist activities toward receptor. Results: Fluorescence polarization assay revealed that PPD(S, R) and PPT(S, R) could bind to hERα-LBD with moderate affinities. In the dual luciferase reporter assay using transiently transfected MCF-7 cells, PPD(S, R) and PPT(S, R) acted as agonists of hERα. Molecular docking results showed that these ginsenosides adopted an agonist conformation in the flexible hydrophobic ligand-binding pocket. The stereostructure of C-20 hydroxyl group and the presence of C-6 hydroxyl group exerted significant influence on the hydrogen bond network and steric hindrance, respectively. Conclusion: This work may provide insight into the chemical and pharmacological screening of novel therapeutic agents from ginsenosides.

• IMMUNE NETWORK
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• v.24 no.3
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• pp.21.1-21.16
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• 2024

IL-1, a pleiotropic cytokine with profound effects on various cell types, particularly immune cells, plays a pivotal role in immune responses. The proinflammatory nature of IL-1 necessitates stringent control mechanisms of IL-1-mediated signaling at multiple levels, encompassing transcriptional and translational regulation, precursor processing, as well as the involvement of a receptor accessory protein, a decoy receptor, and a receptor antagonist. In T-cell immunity, IL-1 signaling is crucial during both the priming and effector phases of immune reactions. The fine-tuning of IL-1 signaling hinges upon two distinct receptor types; the functional IL-1 receptor (IL-1R) 1 and the decoy IL-1R2, accompanied by ancillary molecules such as the IL-1R accessory protein (IL-1R3) and IL-1R antagonist. IL-1R1 signaling by IL-1β is critical for the differentiation, expansion, and survival of Th17 cells, essential for defense against extracellular bacteria or fungi, yet implicated in autoimmune disease pathogenesis. Recent investigations emphasize the physiological importance of IL-1R2 expression, particularly in its capacity to modulate IL-1-dependent responses within Tregs. The precise regulation of IL-1R signaling is indispensable for orchestrating appropriate immune responses, as unchecked IL-1 signaling has been implicated in inflammatory disorders, including Th17-mediated autoimmunity. This review provides a thorough exploration of the IL-1R signaling complex and its pivotal roles in immune regulation. Additionally, it highlights recent advancements elucidating the mechanisms governing the expression of IL-1R1 and IL-1R2, underscoring their contributions to fine-tuning IL-1 signaling. Finally, the review briefly touches upon therapeutic strategies targeting IL-1R signaling, with potential clinical applications.

• The Bulletin of The Korean Astronomical Society
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• v.45 no.1
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• pp.66.2-66.2
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• 2020

We propose a deep learning analysis technique with a convolutional neural network (CNN) to predict the evolutionary track of the Epoch of Reionization (EoR) from the 21-cm differential brightness temperature tomography images. We use 21cmFAST, a fast semi-numerical cosmological 21-cm signal simulator, to produce mock 21-cm maps between z = 6 ~ 13. We then apply two observational effects, such as instrumental noise and limit of (spatial and depth) resolution somewhat suitable for realistic choices of the Square Kilometre Array (SKA), into the 21-cm maps. We design our deep learning model with CNN to predict the sliced-averaged neutral hydrogen fraction from the given 21-cm map. The estimated neutral fraction from our CNN model has great agreement with the true value even after coarsely smoothing with broad beam size and frequency bandwidth and heavily covered by noise with narrow beam size and frequency bandwidth. Our results show that the deep learning analyzing method has the potential to reconstruct the EoR history efficiently from the 21-cm tomography surveys in future.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.11
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• pp.5229-5252
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• 2016

With proliferation of diverse network access technologies, users demands are also increasing and service providers are offering a Quality of Service (QoS) to satisfy their customers. In roaming, a mobile node (MN) traverses number of available networks in the heterogeneous wireless networks environment and a single operator is not capable to fulfill the demands of user. It is crucial task for MN for selecting a best network from the list of networks at any time anywhere. A MN undergoes a network selection situation frequently when it is becoming away from the home network. Multiple Attribute Group Decision (MAGD) method will be one of the best ways for selecting target network in heterogeneous wireless networks (4G). MAGD network selection process is predominantly dependent on two steps, i.e., attribute weight, decision maker's (DM's) weight and aggregation of opinion of DMs. This paper proposes Multi-Attribute Intuitionistic Fuzzy Group Decision Method (MAIFGDM) using TOPSIS for the selection of the suitable candidate network. It is scalable and is able to handle any number of networks with large set of attributes. This is a method of lower complexity and is useful for real time applications. It gives more accurate result because it uses Intuitionistic Fuzzy Sets (IFS) with an additional parameter intuitionistic fuzzy index or hesitant degree. MAIFGDM is simulated in MATLAB for its evaluation. A comparative study of MAIFDGM is also made with TOPSIS and Fuzzy-TOPSIS in respect to decision delay. It is observed that MAIFDGM have low values of decision time in comparison to TOPSIS and Fuzzy-TOPSIS methods.

• Journal of the Korean institute of surface engineering
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• v.17 no.2
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• pp.29-40
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• 1984

The structure of rapidly solidified Al-Si-based alloys and its relationship to subsequent anodic film growth in near neutral and acid solutions have been investigated. Solidification of the alloys proceeds via pre-dendritic nuclei, associated with rugosity of the casting surface, from which cellular-type growth, comprised of aluminium-rich material surrounded by silicon-containing material, emanates. Observation of ultramicrotomed sections of the alloys and their anodic films reveals the local oxidation of the silicon-rich phase and its incorporation into the anodic alumina film, formed in near neutral solutions. Such incorporation occurs but resultant isolation of the silicon-rich phase is not possible for anodizing in phosphoric acid, and a three-dimensional network of the oxidized silicon-containing phase, with continuing development of porous anodic alumina, is observed.

• 한국정보통신설비학회:학술대회논문집
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• 2009.08a
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• pp.91-96
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• 2009

In Public Switched Telephone Networks(PSTN) number portability is implemented by utilizing Intelligent Network(IN) functions for number mapping. And voice over IP(VoIP) and IP Multimedia Subsystem(IMS) networks can deploy number portability by using E.164 Number Mapping(ENUM). This paper discuss the possibility of using E.164 Number Mapping(ENUM) for number portability in voice over IP/IP Multimedia Subsystem and Public Switched Telephone Networks, eliminating the need for Number Portability Database(NPDB) for number portability routing data in Public Switched Telephone Networks.