• Advances in nano research
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• v.12 no.5
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• pp.529-547
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• 2022

Graphene is one of the strongest, stiffest, and lightest nanoscale materials known to date, making it a potentially viable and attractive candidate for developing lightweight structural composites to prevent high-velocity ballistic impact, as commonly encountered in defense and space sectors. In-plane twist in bilayer graphene has recently revealed unprecedented electronic properties like superconductivity, which has now started attracting the attention for other multi-physical properties of such twisted structures. For example, the latest studies show that twisting can enhance the strength and stiffness of graphene by many folds, which in turn creates a strong rationale for their prospective exploitation in high-velocity impact. The present article investigates the ballistic performance of twisted bilayer graphene (tBLG) nanostructures. We have employed molecular dynamics (MD) simulations, augmented further by coupling gaussian process-based machine learning, for the nanoscale characterization of various tBLG structures with varying relative rotation angle (RRA). Spherical diamond impactors (with a diameter of 25Å) are enforced with high initial velocity (V_i) in the range of 1 km/s to 6.5 km/s to observe the ballistic performance of tBLG nanostructures. The specific penetration energy (E_p^*) of the impacted nanostructures and residual velocity (V_r) of the impactor are considered as the quantities of interest, wherein the effect of stochastic system parameters is computationally captured based on an efficient Gaussian process regression (GPR) based Monte Carlo simulation approach. A data-driven sensitivity analysis is carried out to quantify the relative importance of different critical system parameters. As an integral part of this study, we have deterministically investigated the resonant behaviour of graphene nanostructures, wherein the high-velocity impact is used as the initial actuation mechanism. The comprehensive dynamic investigation of bilayer graphene under the ballistic impact, as presented in this paper including the effect of twisting and random disorder for their prospective exploitation, would lead to the development of improved impact-resistant lightweight materials.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.637-646
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• 2022

In this paper the comparative study on the pronunciation of Vietnamese learners and Koreans for the Korean syllable final double consonants was performed. For many errors and the suggested teaching methods related to the pronunciation of the Korean syllable final double consonants that were investigated and analyzed through linguistic research the results of this study by using the analysis tools of speech signal processing were confirmed. Thus, we suggest the new educational method in this paper. Using SVM, which is widely used in machine learning of artificial intelligence the pronunciation of Vietnamese learners and that of Koreans were compared. Being able to obtain the decision hyperplane of the SVM means that Vietnamese learners' pronunciation of the Korean syllable final double consonants is quite different from that of Koreans. Otherwise their pronunciation are pretty similar each other. The new teaching method presented in this paper is not only composed of writing and listening but is included things such as the speech signal waveform in the time domain and its corresponding energy that can be visualized to the learners.

• Journal of Digital Convergence
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• v.19 no.11
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• pp.85-93
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• 2021

The government is pursuing plans to create new e-government services. In terms of improving business procedures, dBrain (finance), e-people (personnel), and Onnara (electronic payment and business management) have achieved considerable results, and are currently making efforts to improve existing administrative services using newly emerged ICT. Among them, this paper attempted to study whether self-learning-based intelligent administrative services are efficient in the work process of public officials promoting actual work and affect their continued use. Based on individual perceptions and attitudes toward advanced ICTs such as AI, big data, and blockchain, public officials' influences on administrative services were identified and verified using UTAUT variables. They believe that the establishment and introduction of innovative administrative services can be used more efficiently, and they have high expectations for the use and provision of services as ICT develops. In the future, a model will be also applied to citizens

• Journal of Korean Society of Archives and Records Management
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• v.21 no.4
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• pp.101-115
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• 2021

Access re-review is a valuable and important task, but it is burdensome for archivists. Thus, an access re-review automation was proposed to address this. In this situation, the National Archives of Korea actually utilized the access re-review solution in the performance of the "2020 Access Re-Review Project" and compared and analyzed it with human work. The project was, however, not a research project centered on analysis on access re-review solutions, and it has a limited result in terms of experimental use of commercial programs. Nevertheless, in the current situation where there are only macro and superficial discussions on access re-review of intelligent archives, it would be meaningful to apply the access re-review solution to archivists in real businesses and examine the results. This paper seeks to discuss the practicality that can mitigate the task of access re-review through an analysis of use cases of access re-review solutions.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.9-14
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• 2022

In the IoT(internet of things) where various devices can be connected, failure of essential devices may lead to a lot of economic and life losses. For reducing the losses, fault diagnosis techniques have been considered an essential part of IoT. In this paper, the method based on a graph neural network is proposed for determining fault and classifying types by extracting features from vibration data of systems. For training of the deep learning model, fault dataset are used as input data obtained from the CWRU(case western reserve university). To validate the classification performance of the proposed model, a conventional CNN(convolutional neural networks)-based fault classification model is compared with the proposed model. From the simulation results, it was confirmed that the classification performance of the proposed model outweighed the conventional model by up to 5% in the unevenly distributed data. The classification runtime can be improved by lightweight the proposed model in future works.

• Journal of the Korean Society for Library and Information Science
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• v.57 no.3
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• pp.119-141
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• 2023

The COVID-19 pandemic has significantly changed the landscape of knowledge and information services that public libraries around the world have been providing since modern times. In particular, as social distancing has become routine, the contraction of cultural activities and the shift to online platforms have negatively impacted library visitation and use services, greatly reducing borrowing and reading, use of spaces and facilities, interlibrary loan services, program operations, and outreach services. Therefore, this study investigated and analyzed the current status of services provided by public libraries in Korea and abroad in response to COVID-19, and proposed practical ways to improve and expand services in response to COVID-19 based on the results of a survey of librarians' perceptions. Specifically, these include improving the online reservation system for reading and borrowing services and developing and providing various outreach services, acquiring and expanding electronic resources, expanding online program services (reading, culture, lifelong learning, etc.), strengthening library services for vulnerable populations, providing information portal services related to new infectious diseases, strengthening facilities and space provision services, preparing infectious disease response guidelines, and providing education and training to strengthen librarians' capabilities.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.4
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• pp.193-202
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• 2023

In recent years, the outstanding performance of large language models (LLMs) trained on extensive datasets has become a hot topic. Since studies on LLMs are available on open-source approaches, the ecosystem is expanding rapidly. Models that are task-specific, lightweight, and high-performing are being actively disseminated using additional training techniques using pre-trained LLMs as foundation models. On the other hand, the performance of LLMs for Korean is subpar because English comprises a significant proportion of the training dataset of existing LLMs. Therefore, research is being carried out on Korean-specific LLMs that allow for further learning with Korean language data. This paper identifies trends of open source based LLMs and introduces research on Korean specific large language models; moreover, the applications and limitations of large language models are described.

• The Journal of the Acoustical Society of Korea
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• v.41 no.6
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• pp.662-667
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• 2022

Computing music similarity is an indispensable component in developing music search service. This paper focuses on learning a music similarity function in order to boost cover song identification performance. By using the probabilistic linear discriminant analysis, we construct a latent music space where the distances between cover song pairs reduces while the distances between the non-cover song pairs increases. We derive a music similarity function by testing hypothesis, whether two songs share the same latent variable or not, using the probabilistic models with the assumption that observed music features are generated from the learned latent music space. Experimental results performed on two cover music datasets show that the proposed music similarity improves the cover song identification performance.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.434-442
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• 2023

The relationship between acute kidney injury (AKI) prediction and nephrotoxic drugs, or drugs that adversely affect kidney function, is one that has yet to be explored in the critical care setting. One contributing factor to this gap in research is the limited investigation of drug modalities in the intensive care unit (ICU) context, due to the challenges of processing prescription data into the corresponding drug representations and a lack in the comprehensive understanding of these drug representations. This study addresses this gap by proposing a novel approach that leverages patient prescription data as a modality to improve existing models for AKI prediction. We base our research on Electronic Health Record (EHR) data, extracting the relevant patient prescription information and converting it into the selected drug representation for our research, the extended-connectivity fingerprint (ECFP). Furthermore, we adopt a unique multimodal approach, developing machine learning models and 1D Convolutional Neural Networks (CNN) applied to clinical drug representations, establishing a procedure which has not been used by any previous studies predicting AKI. The findings showcase a notable improvement in AKI prediction through the integration of drug embeddings and other patient cohort features. By using drug features represented as ECFP molecular fingerprints along with common cohort features such as demographics and lab test values, we achieved a considerable improvement in model performance for the AKI prediction task over the baseline model which does not include the drug representations as features, indicating that our distinct approach enhances existing baseline techniques and highlights the relevance of drug data in predicting AKI in the ICU setting.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.378-384
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• 2023

In this study, we aim to derive the descriptor vector conditions that can simultaneously achieve the efficiency and accuracy of artificial Neural Network Potentials (NNP). The material system selected is silicon, a highly applicable material in various industries. Atomic structure-dependent energy data for training artificial neural networks were generated through density functional theory calculations. Behler-Parrinello type atomic-centered symmetric functions were employed as descriptors, and various length vector NNPs were generated. These NNPs were applied to reproduce the structure and mechanical properties of silicon materials in molecular dynamics simulations. In our findings, the minimum vector length for achieving both learning and computational efficiency while maintaining property reproducibility is approximately 50. It was also observed that, for the same conditions, incorporating more angle-dependent symmetric functions into the descriptor vector, could enhance the accuracy of NNP. Our results can provide guidelines for optimizing the conditions of descriptor vectors to achieve both efficiency and accuracy of NNP, simultaneously.