• Journal of the Korean Recycled Construction Resources Institute
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• v.10 no.3
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• pp.185-194
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• 2022

Coffee is one of the most consumed beverages in the world and is the second largest traded commodity after petroleum. Due to the great demand of this product, large amounts of waste is generated in the coffee industry, which are toxic and represent serious environmental problems. This study aims to study the possibility of recycling spent coffee grounds (SCG) as a mineral admixture by replacing the cement in the manufacturing of concrete. To recycle the coffee g rounds, the SCG was dried to remove moisture and fired in a kiln at 850 ℃ for 8 hours. Carbonized coffee grounds are produced as coffee grounds ash (CGA) through ball mill grinding. The chemical composition of the prepared coffee grounds ash was investigated using X-ray fluorescence (XFR). According to the chemical composition analysis, the major elements of coffee grounds ash are K₂O(51.74 %), CaO(15.92 %), P₂O₅(14.39 %), MgO(7.74 %) and SO₃(6.89 %), with small amounts of F₂O₃(0.66 %), SiO₂(0.59 %) and Al₂O₃(0.31 %) content. To evaluate quality and mechanical properties, substitutions of 5, 10, and 15 wt.% of coffee grounds ash (CGA) were tested. From the quality test results, the 28-day activity index of CGA5 reached 80 %, and the flow value ratio reached 96 %, which is comparable to the minimum requirement for second-grade FA. From the test results of the mortar, the optimal results have been found in specimens with 5 wt-% coffee grounds ash, showing good mechanical and physical properties.

• Applied Chemistry for Engineering
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• v.33 no.2
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• pp.126-132
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• 2022

Alkaline fuel cells using liquid fuels such as hydrazine and ammonia are gaining great attention as a clean and renewable energy solution possibly owing to advantages such as excellent energy density, simple structure, compact size in fuel container, and ease of storage and transportation. However, common shortcomings including cathode flooding, fuel crossover, side yield reactions, and fuel security and toxicity are still challenging issues. Real time monitoring of fuel concentrations integrated into a fuel cell device can help improving fuel cell performance via predicting any loss of fuels used at a cathode for efficient energy production. There have been extensive research efforts made on developing real-time sensing platforms for hydrazine and ammonia. Among these, recent advancements in electrochemical sensors offering high sensitivity and selectivity, easy fabrication, and fast monitoring capability for analysis of hydrazine and ammonia concentrations will be introduced. In particular, research trend on the integration of metallic and metal oxide nanoparticles and also their hybrids with carbon-based nanomaterials into electrochemical sensing platforms for improvement in sensitivity and selectivity will be highlighted.

• Journal of Korean Home Economics Education Association
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• v.34 no.1
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• pp.1-16
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• 2022

This study was performed to develop educational materials in the form of card news that can be easily accessed on mobile phones or the Internet for milk intake education of the elderly based on the scientific evidence and their needs. The themes included in the card news were selected based on the literature and focus group interviews with 10 elderly individuals (78.10±6.66 years old). For the selected themes, information that elderly users most want to know was selected for the purpose of effective communication, while reflecting the eating habits, lifestyle, living environment, and nutrition and health status of the elderly in Korea. The draft of the card news was reviewed by the researcher, consulted by experts, and surveyed with 50 elderly individuals (70.44±5.16 years old). Based on the results of the review, consultations, and the survey, a final draft of the card news consisting of 12 pages was completed. The card news of the present study is expected to be an effective educational material considering the high level of satisfaction (higher than 4 on the 5-point scales) indicated by the survey respondents. Therefore this card news is expected to help increase milk intake through friendly milk education for the elderly.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.4
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• pp.63-80
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• 2022

As a technology to analyze and predict an issue has been developed by constructing real space into virtual space, it is becoming more important to acquire precise spatial information in complex cities. In this study, images were acquired using an unmanned aerial vehicle for urban area with complex landscapes, and land cover classification was performed object-based image analysis and semantic segmentation techniques, which were image classification technique suitable for high-resolution imagery. In addition, based on the imagery collected at the same time, the replicability of land cover classification of each artificial intelligence (AI) model was examined for areas that AI model did not learn. When the AI models are trained on the training site, the land cover classification accuracy is analyzed to be 89.3% for OBIA-RF, 85.0% for OBIA-DNN, and 95.3% for U-Net. When the AI models are applied to the replicability assessment site to evaluate replicability, the accuracy of OBIA-RF decreased by 7%, OBIA-DNN by 2.1% and U-Net by 2.3%. It is found that U-Net, which considers both morphological and spectroscopic characteristics, performs well in land cover classification accuracy and replicability evaluation. As precise spatial information becomes important, the results of this study are expected to contribute to urban environment research as a basic data generation method.

• Journal of the Korean Electrochemical Society
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• v.25 no.1
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• pp.1-12
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• 2022

Lithium metal is the most promising anode for next-generation lithium-ion batteries due to its lowest reduction potential (-3.04 V vs. SHE) and high specific capacity (3860 mAh/g). However, the dendritic formation under high charging current density remains one of main technical barriers to be used for commercial rechargeable batteries. To address these issues, tremendous research to suppress lithium dendrite formation have been conducted through new electrolyte formulation, robust protection layer, shape-controlled lithium metal, separator modification, etc. However, Li/Li symmetric cell test is always a starting or essential step to demonstrate better lithium dendrite formation behavior with lower overpotential and longer cycle life without careful analysis. Thus, this review summarizes overpotential behaviors of Li/Li symmetric cells along with theoretical explanations like initial peaking or later arcing. Also, we categorize various overpotential data depending on research approaches and discuss them based on peaking and arcing behaviors. Thus, this review will be very helpful for researchers in lithium metal to analyze their overpotential behaviors.

• KNOM Review
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• v.23 no.2
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• pp.29-41
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• 2020

Networks are growing faster than ever before causing a multi-domain complexity. The diversity, variety and dynamic nature of network traffic and services require enhanced orchestration and management approaches. While many standard orchestrators and network operators are resulting in an increase of complexity for handling E2E slice orchestration. Besides, there are multiple domains involved in E2E slice orchestration including access, edge, transport and core network each having their specific challenges. Hence, handling of multi-domain, multi-platform and multi-operator based networking environments manually requires specified experts and using this approach it is impossible to handle the dynamic changes in the network at runtime. Also, the manual approaches towards handling such complexity is always error-prone and tedious. Hence, this work proposes an automated and abstracted solution for handling E2E slice orchestration using an intent-based approach. It abstracts the domains from the operators and enable them to provide their orchestration intention in the form of high-level intents. Besides, it actively monitors the orchestrated resources and based on current monitoring stats using the machine learning it predicts future utilization of resources for updating the system states. Resulting in a closed-loop automated E2E network orchestration and management system.

• Journal of Platform Technology
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• v.10 no.4
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• pp.47-55
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• 2022

Recently, diverse devices using different wireless technologies are gradually increasing in the IoT environment. In particular, it is essential to design an efficient feature extraction approach and detect the exact types of radio signals in order to accurately identify various radio signal modulation techniques. However, it is difficult to gather labeled wireless signal in a real environment due to the complexity of the process. In addition, various learning techniques based on deep learning have been proposed for wireless signal classification. In the case of deep learning, if the training dataset is not enough, it frequently meets the overfitting problem, which causes performance degradation of wireless signal classification techniques using deep learning models. In this paper, we propose a generative adversarial network(GAN) based on data augmentation techniques to improve classification performance when various wireless signals exist. When there are various types of wireless signals to be classified, if the amount of data representing a specific radio signal is small or unbalanced, the proposed solution is used to increase the amount of data related to the required wireless signal. In order to verify the validity of the proposed data augmentation algorithm, we generated the additional data for the specific wireless signal and implemented a CNN and LSTM-based wireless signal classifier based on the result of balancing. The experimental results show that the classification accuracy of the proposed solution is higher than when the data is unbalanced.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.3
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• pp.12-36
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• 2023

Graph databases, which store different types of data and their relationships modeled as a graph, can be effective in managing and analyzing real estate spatial data linked by complex relationships. However, they are not widely used due to the limited spatial functionalities of graph databases. In this study, we propose a uniform grid-based real estate spatial data management approach using a graph database to respond to various real estate-related spatial questions. By analyzing the real estate community to identify relevant data and utilizing national point numbers as unit grids, we construct a graph schema that linking diverse real estate data, and create a test database. After building a test database, we tested basic topological relationships and spatial functions using the Jackpine benchmark, and further conducted query tests based on various scenarios to verify the appropriateness of the proposed method. The results show that the proposed method successfully executed 25 out of 29 spatial topological relationships and spatial functions, and achieved about 97% accuracy for the 25 functions and 15 scenarios. The significance of this study lies in proposing an efficient data integration method that can respond to real estate-related spatial questions, considering the limited spatial operation capabilities of graph databases. However, there are limitations such as the creation of incorrect spatial topological relationships due to the use of grid-based indexes and inefficiency of queries due to list comparisons, which need to be improved in follow-up studies.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.8
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• pp.355-364
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• 2023

As advanced cyber threats continue to increase in recent years, it is difficult to detect new types of cyber attacks with existing pattern or signature-based intrusion detection method. Therefore, research on anomaly detection methods using data learning-based artificial intelligence technology is increasing. In addition, supervised learning-based anomaly detection methods are difficult to use in real environments because they require sufficient labeled data for learning. Research on an unsupervised learning-based method that learns from normal data and detects an anomaly by finding a pattern in the data itself has been actively conducted. Therefore, this study aims to extract a latent vector that preserves useful sequence information from sequence log data and develop an anomaly detection learning model using the extracted latent vector. Word2Vec was used to create a dense vector representation corresponding to the characteristics of each sequence, and an unsupervised autoencoder was developed to extract latent vectors from sequence data expressed as dense vectors. The developed autoencoder model is a recurrent neural network GRU (Gated Recurrent Unit) based denoising autoencoder suitable for sequence data, a one-dimensional convolutional neural network-based autoencoder to solve the limited short-term memory problem that GRU can have, and an autoencoder combining GRU and one-dimensional convolution was used. The data used in the experiment is time-series-based NGIDS (Next Generation IDS Dataset) data, and as a result of the experiment, an autoencoder that combines GRU and one-dimensional convolution is better than a model using a GRU-based autoencoder or a one-dimensional convolution-based autoencoder. It was efficient in terms of learning time for extracting useful latent patterns from training data, and showed stable performance with smaller fluctuations in anomaly detection performance.

• Journal of The Korean Association For Science Education
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• v.43 no.4
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• pp.369-387
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• 2023

This study aims to interpret the experience of science research in a graduate school laboratory from the perspective of Gilles Deleuze's concepts of "agencement" and "becoming". The research was conducted as an autobiographical narrative inquiry. The research text is written in a way that tells the story of my science research experience and retells it from the perspective of Gilles Deleuze. In Deleuze's view, science research is a constantly flowing agencement. The science research agencement is composed of a mechanical agencement of various experimental tools-machines and researcher-machines as well as a collective agencement of speech acts such as biological knowledge, experiment protocols, and laboratory rules. Furthermore, science research agencement is fluid as events occur all over the agencement. Data, as a change occurring in the material dimension, is an event and sign that raises problems. It has the agency to influence agencement through an intersubjective relationship with researchers, and the meaning of data is generated in this process. The change of agencement compelled me to perform science practice. I have performed repeated science practice, meaning that my body has constantly been connected to other machines. As a result of this connection, my body has been affected, and the capacity of my body that constitutes the agencement has been augmented. In addition, I was able to be deterritorialized from the existing science research agencement and reterritorialized in a new science research agencement with data. This process of differentiation allowed me to becoming-scientist. In sum, this study provides implications for science practice-oriented education by exploring the process of becoming-scientist based on my science research experience.