• Journal of IKEEE
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• v.26 no.4
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• pp.633-638
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• 2022

Rencently, neuromorphic systems of spiking neural networks (SNNs) that imitate the human brain have attracted attention. Neuromorphic technology has the advantage of high speed and low power consumption in cognitive applications and processing. Resistive random-access memory (RRAM) for SNNs are the most efficient structure for parallel calculation and perform the gradual switching operation of spike-timing-dependent plasticity (STDP). RRAM as synaptic device operation has low-power processing and expresses various memory states. However, the integration of RRAM device causes high switching voltage and current, resulting in high power consumption. To reduce the operation voltage of the RRAM, it is important to develop new materials of the switching layer and metal electrode. This study suggested a optimized new structure that is the Metal/Al₂O₃/HfO_x/SWCNTs/N+silicon (MOCS) with single-walled carbon nanotubes (SWCNTs), which have excellent electrical and mechanical properties in order to lower the switching voltage. Therefore, we show an improvement in the gradual switching behavior and low-power I/V curve of SWCNTs-based memristors.

• Journal of Nutrition and Health
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• v.55 no.3
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• pp.330-347
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• 2022

Folate, a water-soluble vitamin, acts as a coenzyme for one-carbon metabolism in nucleic acid synthesis and amino acid metabolism. Adequate folate nutritional status during the periconceptional period is known to prevent neural tube defects. In addition, insufficient folate intake is associated with various conditions, such as anemia, hyperhomocysteinemia, cardiovascular disease, cancer, cognitive impairment, and depression. This review discusses the rationale for the revision of the 2020 Korean dietary reference intakes for folate, and suggestions for future revisions. Based on the changes in the standard body weight in 2020, the adequate intake (AI) for infants (5-11 months) and the estimated average requirements (EARs) for 15-18 years of age were revised, but there were no changes in the recommended nutrient intakes (RNIs) and tolerable upper intake levels (ULs) for all age groups. Mean folate intake did not reach RNI in most age groups and was particularly low in women aged 15-29 years, according to the results of the 2016-2018 Korea National Health and Nutrition Examination Survey (KNHANES). The percentages of folate intake to RNI were lower than 60% in pregnant and lactating women, but serum folate concentrations were higher than those in other age groups, presumably due to the use of supplements. Therefore, total folate intake, from both food and supplements, should be evaluated. In addition, the database of folate in raw, cooked, and fortified foods should be further expanded to accurately assess the folate intake of Koreans. Determination of the concentrations of erythrocyte folate and plasma homocysteine as well as serum folate is recommended, and quality control of the analysis is critical.

• Journal of Life Science
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• v.32 no.1
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• pp.23-28
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• 2022

CopA3 (LLCIALRKK), an antimicrobial peptide isolated from the Korean dung beetle, has been shown to suppress apoptosis in various cell types. CopA3 inhibits not only bacterial toxin-induced colonocyte apoptosis but also 6-hydroxy dopamine-induced neural cell apoptosis. Our recent study revealed that CopA3 directly binds to caspases (key regulators of apoptosis) and inhibits the proteolytic cleavage required for their activation. But molecular mechanisms underlying CopA3-mediated inhibition of apoptosis in multiple cell types remain unknown. Here we assessed possible effects of CopA3 on expression of survivin, which is known to inhibit apoptosis. In HT29 human colonocytes, CopA3 exposure markedly upregulated survivin expression in a concentration- and time-dependent manner. RT-PCR revealed that CopA3-mediated upregulation of survivin was attributable to increased gene transcription, and further showed that CopA3 also increased expression of Sp1, one of many transcription factors known to be involved in transcription of the survivin gene. Notably, blocking Sp1 by treatment with the Sp1 inhibitor, tolfenamic acid, significantly reduced CopA3-mediated upregulation of survivin. These results collectively suggest that CopA3 induces Sp1 expression, which in turn is involved in upregulation of survivin in human colonocytes. These novel findings establish another pathway for explaining the anti-apoptotic effects of CopA3 against various cellular apoptosis systems.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1167-1181
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• 2021

In relation to the algae bloom, four types of blue-green algae that emit toxic substances are designated and managed as harmful Cyanobacteria, and prediction information using a physical model is being also published. However, as algae are living organisms, it is difficult to predict according to physical dynamics, and not easy to consider the effects of numerous factors such as weather, hydraulic, hydrology, and water quality. Therefore, a lot of researches on algal bloom prediction using machine learning have been recently conducted. In this study, the characteristic importance of water quality factors affecting the occurrence of Cyanobacteria harmful algal blooms (CyanoHABs) were analyzed using the random forest (RF) model for Bohyeonsan Dam and Yeongcheon Dam located in Yeongcheon-si, Gyeongsangbuk-do and also predicted the occurrence of harmful blue-green algae using the machine learning and deep learning models and evaluated their accuracy. The water temperature and total nitrogen (T-N) were found to be high in common, and the occurrence prediction of CyanoHABs using artificial neural network (ANN) also predicted the actual values closely, confirming that it can be used for the reservoirs that require the prediction of harmful cyanobacteria for algal management in the future.

• Journal of Industrial Convergence
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• v.20 no.2
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• pp.1-9
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• 2022

As the need for improvement of transparency and fairness in the selection of national R&D projects has been continuously raised, we analyzed the impact on the evaluation selection results by evaluation indexes for The land transportation technology commercialization support project and searched for ways to improve indexes using the analysis results. As for the research data, it were applied as selection results of new R&D projects and evaluation indexes in two fields(SME innovation and start-up) in 2021. Logistic regression analysis is used for the influence of each evaluation indexes on the evaluation result, and for the regression model, evaluation indexes with low influence are removed in advance through artificial neural network multiple perceptron analysis to improve the reliability of the analysis results. As a result of the analysis, in the field of SME innovation, the influence of the evaluation index on the workforce planning was the lowest and the influence of the appropriateness of commercialization promotion plan was the highest. In the start-up field, the influence of the evaluation indexes for technology development suitability, marketability, and suitability for carrying out the project were estimated to be similar to each other, and the influence of the technology evaluation index was found to be the lowest. The analysis results of this thesis suggest the need for continuous improvement of selection and evaluation indexes, and by using the analysis results to select a fair R&D institution according to the selection of appropriate indexes, it will be possible to contribute to deriving excellent research results and fostering excellent companies in the field of land transportation.

• International journal of advanced smart convergence
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• v.11 no.1
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• pp.19-27
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• 2022

Across the world, 'housing' comprises a significant portion of wealth and assets. For this reason, fluctuations in real estate prices are highly sensitive issues to individual households. In Korea, housing prices have steadily increased over the years, and thus many Koreans view the real estate market as an effective channel for their investments. However, if one purchases a real estate property for the purpose of investing, then there are several risks involved when prices begin to fluctuate. The purpose of this study is to design a real estate price 'return rate' prediction model to help mitigate the risks involved with real estate investments and promote reasonable real estate purchases. Various approaches are explored to develop a model capable of predicting real estate prices based on an understanding of the immovability of the real estate market. This study employs the LSTM method, which is based on artificial intelligence and deep learning, to predict real estate prices and validate the model. LSTM networks are based on recurrent neural networks (RNN) but add cell states (which act as a type of conveyer belt) to the hidden states. LSTM networks are able to obtain cell states and hidden states in a recursive manner. Data on the actual trading prices of apartments in autonomous districts between January 2006 and December 2019 are collected from the Actual Trading Price Disclosure System of the Ministry of Land, Infrastructure and Transport (MOLIT). Additionally, basic data on apartments and commercial buildings are collected from the Public Data Portal and Seoul Metropolitan Government's data portal. The collected actual trading price data are scaled to monthly average trading amounts, and each data entry is pre-processed according to address to produce 168 data entries. An LSTM model for return rate prediction is prepared based on a time series dataset where the training period is set as April 2015~August 2017 (29 months), the validation period is set as September 2017~September 2018 (13 months), and the test period is set as December 2018~December 2019 (13 months). The results of the return rate prediction study are as follows. First, the model achieved a prediction similarity level of almost 76%. After collecting time series data and preparing the final prediction model, it was confirmed that 76% of models could be achieved. All in all, the results demonstrate the reliability of the LSTM-based model for return rate prediction.

• Geophysics and Geophysical Exploration
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• v.25 no.1
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• pp.26-37
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• 2022

Recently, multiple methods using small aircraft for geophysical exploration have been suggested as a result of the development of information and communication technology. In this study, we introduce the hybrid unmanned aircraft electromagnetic system of the Korea Institute of Geosciences and Mineral resources, which is under development. Additionally, data processing and interpretation methods are suggested via the analysis of datasets obtained using the system under development to verify the system. Because the system uses a three-component receiver hanging from a drone, the effects of rotation on the obtained data are significant and were therefore corrected using a rotation matrix. During the survey, the heights of the source and the receiver and their offsets vary in real time and the measured data are contaminated with noise. The noise makes it difficult to interpret the data using the conventional method. Therefore, we developed a recurrent neural network (RNN) model to enable rapid predictions of the apparent resistivity using magnetic field data. Field data noise is included in the training datasets of the RNN model to improve its performance on noise-contaminated field data. Compared with the results of the electrical resistivity survey, the trained RNN model predicted similar apparent resistivities for the test field dataset.

• Journal of Internet Computing and Services
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• v.23 no.2
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• pp.53-59
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• 2022

After the 3rd level autonomous driving vehicle, the 4th and 5th level of autonomous driving technology is trying to maintain the optimal condition of the passengers as well as the perfect driving of the vehicle. However current autonomous driving technology is too dependent on visual information such as LiDAR and front camera, so it is difficult to fully autonomously drive on roads other than designated roads. Therefore this paper proposes a Braking Strength Calculation System (BSCS), in which a vehicle classifies road conditions using data other than visual information and calculates optimal braking strength according to road conditions and driving conditions. The BSCS consists of RCDM (Road Condition Definition Module), which classifies road conditions based on KNN algorithm, and BSCM (Braking Strength Calculation Module), which calculates optimal braking strength while driving based on current driving conditions and road conditions. As a result of the experiment in this paper, it was possible to find the most suitable number of Ks for the KNN algorithm, and it was proved that the RCDM proposed in this paper is more accurate than the unsupervised K-means algorithm. By using not only visual information but also vibration data applied to the suspension, the BSCS of the paper can make the braking of autonomous vehicles smoother in various environments where visual information is limited.

• KNOM Review
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• v.23 no.2
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• pp.18-28
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• 2020

With the development of the virtual community, the benefits that IT technology provides to people in fields such as healthcare, industry, communication, and culture are increasing, and the quality of life is also improving. Accordingly, there are various malicious attacks targeting the developed network environment. Firewalls and intrusion detection systems exist to detect these attacks in advance, but there is a limit to detecting malicious attacks that are evolving day by day. In order to solve this problem, intrusion detection research using machine learning is being actively conducted, but false positives and false negatives are occurring due to imbalance of the learning dataset. In this paper, a Random Oversampling method is used to solve the unbalance problem of the UNSW-NB15 dataset used for network intrusion detection. And through experiments, we compared and analyzed the accuracy, precision, recall, F1-score, training and prediction time, and hardware resource consumption of the models. Based on this study using the Random Oversampling method, we develop a more efficient network intrusion detection model study using other methods and high-performance models that can solve the unbalanced data problem.

• Environmental and Resource Economics Review
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• v.31 no.3
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• pp.419-449
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• 2022

This paper aims to quantify the potential economic burdens of EU's carbon border adjustment mechanisms faced by Korean domestic industries. In addition, this study tries to compare and analyzes changes in the burden of each industry resulted from the implementation of the domestic low-carbon policy. Based on the quantitative findings, we intend to suggest policy implications for establishing mid- to long-term strategies in response to climate change risks. Based on the environmentally extended input-output analysis, the total economic burdens of the domestic industries due to the EU's carbon border adjustment mechanisms are estimated to be approximately KRW 8,245.6 billion in 2030. Looking at the impacts by industry, it is found that major industries such as petrochemicals, petroleum refining, transportation equipment, steel, automobiles, and electric/electronic equipment industries are expected to account for 84.3% of the total potential burdens. In addition, in multiple policy scenarios assuming technological developments and energy transition following the implementation of domestic low-carbon policies, the total economic burden of carbon border adjustment is expected to decrease by about 11.7% to 15.0%. The main result of this study suggests that we should not view EU EU's carbon border adjustment mechanism as a trade regulation, but to use it as a momentum for more effective implementation of the low-carbon and energy transition strategies in the global carbon neural era.