• The Mathematical Education
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• v.62 no.3
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• pp.435-455
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• 2023

This study primarily focused on the development of an Explainable Artificial Intelligence (XAI) model to discern and analyze papers with significant impact in the field of mathematics education. To achieve this, meta-information from 29 domestic and international mathematics education journals was utilized to construct a comprehensive academic research network in mathematics education. This academic network was built by integrating five sub-networks: 'paper and its citation network', 'paper and author network', 'paper and journal network', 'co-authorship network', and 'author and affiliation network'. The Random Forest machine learning model was employed to evaluate the impact of individual papers within the mathematics education research network. The SHAP, an XAI model, was used to analyze the reasons behind the AI's assessment of impactful papers. Key features identified for determining impactful papers in the field of mathematics education through the XAI included 'paper network PageRank', 'changes in citations per paper', 'total citations', 'changes in the author's h-index', and 'citations per paper of the journal'. It became evident that papers, authors, and journals play significant roles when evaluating individual papers. When analyzing and comparing domestic and international mathematics education research, variations in these discernment patterns were observed. Notably, the significance of 'co-authorship network PageRank' was emphasized in domestic mathematics education research. The XAI model proposed in this study serves as a tool for determining the impact of papers using AI, providing researchers with strategic direction when writing papers. For instance, expanding the paper network, presenting at academic conferences, and activating the author network through co-authorship were identified as major elements enhancing the impact of a paper. Based on these findings, researchers can have a clear understanding of how their work is perceived and evaluated in academia and identify the key factors influencing these evaluations. This study offers a novel approach to evaluating the impact of mathematics education papers using an explainable AI model, traditionally a process that consumed significant time and resources. This approach not only presents a new paradigm that can be applied to evaluations in various academic fields beyond mathematics education but also is expected to substantially enhance the efficiency and effectiveness of research activities.

• Microbiology and Biotechnology Letters
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• v.37 no.3
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• pp.248-257
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• 2009

The inhibitors against Vibrio harveyi quorum sensing (QS) signaling were developed by modifying the molecular structure of the major signal, N-3-hydroxybutanoyl-L-homoserine lactone (3-OH-$C_4$-HSL). A series of structural derivatives, N-(3-hydroxysulfonyl)-L-homoserine lactones (HSHLs) were synthesized by the solid-phase organic synthesis method. The in vivo QS inhibition by these compounds was measured by a bioassay system using the V. harveyi bioluminescence, and all showed significant inhibitory effects. To analyze the interaction between these compounds and LuxN, a 3-OH-$C_4$-HSL receptor protein of V. harveyi, we tentatively determined the putative signal binding domain of LuxN based on the sequence homology with other acyl-HSL binding proteins, and predicted the partial 3-D structure of the putative signal binding domain of LuxN by using ORCHESTRA program, and further estimated the binding poses and energies (docking scores) of 3-OH-$C_4$-HSL and HSHLs within the domain. In comparison of the result from this modeling study with that of in vivo bioassay, we suggest that the in silica interpretation of the interaction between ligands and their receptor proteins can be a valuable way to develop better competitive inhibitors, especially in the case that the structural information of the protein is limited.

• Sleep Medicine and Psychophysiology
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• v.5 no.2
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• pp.210-215
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• 1998

OSA(Obstructive Sleep Apnea) is a condition with repeated obstruction of the upper airway while sleeping. This obstruction of the upper airway may result if sleep-induced physiologic change in muscle activity is superimposed on various structural defects of the upper airway. Macroglossia, micrognathia, retrognathia, and decreased airway lumen have all been implicated in OSA. There are a variety of surgical and nonsurgical treatment modalities currently available for OSA. Recently the uvulopalatopharyngoplasty has achieved widespread use, but success rates vary and long-term effects are still unknown. Current nonsurgical treatment methods can be cumbersome and uncomfortable with variable responses. For this case. we diagnosed as OSA by using polysomnographic analysis and cephalometric analysis, and delivered intraoral removable appliance which is more conservative, reversible and easy to use for patient. And we got results that Apnea Index(AI) decreased from 14.4 to 3.1, Total Respiratory Disturbance Index(Total RDI) decreased from 18.1 to 7.0 and removal of chronic headache in daytime.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1281-1284
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• 2015

Multi-day trip itinerary planning is complex and time consuming task, from selecting a list of worth visiting POIs to arranging them into an itinerary with various constraints and requirements. In this paper, we present CYTRIP, a multi-day trip itinerary planning system that engages human computation (i.e. crowd recommendation) to collaboratively recommend POIs by providing a shared workspace. CYTRIP takes input the collective intelligence of crowd (i.e. recommended POIs) to build a multi-day trip itinerary taking into account user's preferences, various time constraints and locations. Furthermore, we explain how we engage crowd in our system. The planning problem and domain are formulated as AI planning using PDDL3. The preliminary empirical experiments show that our domain formulation is applicable to both single-day and multi-day trip planning.

• ETRI Journal
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• v.45 no.2
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• pp.346-357
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• 2023

Hardware security primitives, also known as physical unclonable functions (PUFs), perform innovative roles to extract the randomness unique to specific hardware. This paper proposes a novel hardware security primitive using a commercial off-the-shelf flash memory chip that is an intrinsic part of most commercial Internet of Things (IoT) devices. First, we define a hardware security source model to describe a hardware-based fixed random bit generator for use in security applications, such as cryptographic key generation. Then, we propose a hardware security primitive with flash memory by exploiting the variability of tunneling electrons in the floating gate. In accordance with the requirements for robustness against the environment, timing variations, and random errors, we developed an adaptive extraction algorithm for the flash PUF. Experimental results show that the proposed flash PUF successfully generates a fixed random response, where the uniqueness is 49.1%, steadiness is 3.8%, uniformity is 50.2%, and min-entropy per bit is 0.87. Thus, our approach can be applied to security applications with reliability and satisfy high-entropy requirements, such as cryptographic key generation for IoT devices.

• Journal of Distribution Science
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• v.22 no.3
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• pp.83-92
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• 2024

Purpose: With the rapid information distribution of sport-related SNS, sport industry has utilized SNS as technical solution to distribute sport and health-related information. The current study examined the effect of SNS information use for running-specific content on running engagement and psychological well-being. Research design, data and methodology: Data were collected via online survey of participants in marathon events of United States. Descriptive statistics and Chi-square test were conducted to compare demographics and psycho-behavioral outcomes among SNS information users and non-users for running-specific contents. Multivariate hierarchical regression was next employed to examine research hypotheses. Results: A mass-participant running event was used to test seven hypotheses related to the potential role of SNS for running content in promoting running engagement and life satisfaction. In general, findings revealed that use of SNS for running content along with frequency of use can perhaps facilitate running intensity and influence participation in running-related outdoor activities. Furthermore, while overall life satisfaction did not appear to be influenced by use of SNS for running content, there was a demonstrated influence on the individual satisfaction domains. Conclusions: The findings of current study suggest that sport SNS is information distribution media enhancing users' engagement and their six life satisfaction domains. Further implications were discussed.

• Knowledge Management Research
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• v.21 no.3
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• pp.229-247
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• 2020

In the remarkable growth of P2P financial platform in the field of knowledge management, only companies with big data and machine learning technologies are surviving in fierce competition. The ability to analyze borrowers' credit is most important, and platform companies are also recognizing this capability as the most important business asset, so they are building a credit evaluation system based on artificial intelligence. Nonetheless, online P2P platform providers that offer related services only act as intermediaries to apply for investors and borrowers, and all the risks associated with the investments are attributable to investors. For investors, the only way to verify the safety of investment products depends on the reputation of P2P companies from newspaper and online website. Time series information such as delinquency rate is not enough to evaluate the early stage of Korean P2P makers' credit analysis capability. This study examines the credit analysis procedure of P2P loan platform using artificial intelligence through the case analysis method for well known the top three companies that are focusing on the credit lending market and the kinds of information data to use. Through this, we will improve the understanding of credit analysis techniques through artificial intelligence, and try to examine limitations of credit analysis methods through artificial intelligence.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1693-1705
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• 2023

Rural areas, which account for about 90% of the country's land area, are increasing in importance and value as a space that performs various public functions. However, facilities that adversely affect residents' lives, such as livestock facilities, factories, and solar panels, are being built indiscriminately near residential areas, damaging the rural environment and landscape and lowering the quality of residents' lives. In order to prevent disorderly development in rural areas and manage rural space in a planned manner, detection and monitoring of hazardous facilities in rural areas is necessary. Data can be acquired through satellite imagery, which can be acquired periodically and provide information on the entire region. Effective detection is possible by utilizing image-based deep learning techniques using convolutional neural networks. Therefore, U-Net model, which shows high performance in semantic segmentation, was used to classify potentially hazardous facilities in rural areas. In this study, KOMPSAT ortho-mosaic optical imagery provided by the Korea Aerospace Research Institute in 2020 with a spatial resolution of 0.7 meters was used, and AI training data for livestock facilities, factories, and solar panels were produced by hand for training and inference. After training with U-Net, pixel accuracy of 0.9739 and mean Intersection over Union (mIoU) of 0.7025 were achieved. The results of this study can be used for monitoring hazardous facilities in rural areas and are expected to be used as basis for rural planning.

• Bulletin of the Korean Chemical Society
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• v.33 no.5
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• pp.1675-1680
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• 2012

Three new cyanide-bridged $Cr^{III}Mn^{II}$ binuclear complexes, $[Mn(phen)_2Cl][Cr(bpmb)(CN)_2]{\cdot}H_2O$ ($\mathbf{1}$) (phen = 1,10-phenanthroline, $bpdmb^{2-}$ = 1,2-bis(pyridine-2-carboxamido)-4-methyl-benzenate), $[Mn(phen)_2Cl][Cr(bpmb)-(CN)_2]{\cdot}H_2O$ ($\mathbf{2}$) ($bpdmb^{2-}$ = 1,2-bis(pyridine-2-carboxamido)-4,5-dimethyl-benzenate), and $[Mn(phen)_2Cl]-[Cr(bpClb)(CN)_2]{\cdot}CH_3OH{\cdot}H_2O$ ($\mathbf{3}$) ($bpClb^{2-}$ = 1,2-bis(pyridine-2-carboxamido)-4-chloro-benzenate) were obtained based on $Mn(phen)_2Cl_2$ and a series of dicyanidechromate(III) building blocks. Single crystal X-ray diffraction analysis shows the structures of the three complexes are dimeric type with two different metal centers linked by a cyanide group from corresponding dicyanidechromate(III) building block. Magnetic investigations indicate the existence of relatively weak antiferromagnetic coupling between Cr(III) and Mn(II) ions with best-fit constants $J_{CrMn}=-2.78(5)cm^{-1}$ for $\mathbf{1}$, $J_{CrMn}=-3.02(2)cm^{-1}$ for $\mathbf{2}$ and $J_{CrMn}=-2.27(3)cm^{-1}$ for $\mathbf{3}$ based on the spin exchange Hamiltonian = $-2J_{CrMn}\hat{S}_{Cr}\hat{S}_{Mn}$. The magneto-structural correlation of cyanide-bridged $Cr^{III}Mn^{II}$ complexes has been discussed at last.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2010.06a
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• pp.40-40
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• 2010

Structural, electrical, and optical properties of atomic layer-controlled AI-doped ZnO (ZnO:Al) films grown on glass by atomic layer deposition (ALD) were characterized with various $Al_2O_3$ film contents for use as transparent electrodes. Unlike films made using sputtering methods, the diffraction peak position of the films grown by ALD based on alternate self-limiting surface chemical reactions moved progressively to a wider angle (red shift) with increasing $Al_2O_3$ film content, which seems to be evidence of Zn substitution in the film by layer-by-layer growth. By adjusting the $Al_2O_3$ film content, the electrical resistivity of ZnO:Al film with the $Al_2O_3$ film content of 2.96% reached the lowest electrical resistivity of $9.80{\times}10^{-4}\Omega{\cdot}cm$, in which the carrier mobility, carrier concentration, and optical transmittance were $11.20\;cm^2V^{-1}s^{-1}$, $5.69{\times}10^{20}\;cm^{-3}$, and 94.23%, respectively. Moreover, the estimated figure of merit value for the transparent conductive oxide applications from our best sample was $7.7\;m{\Omega}^{-1}$.