• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.987-995
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• 2022

Deep learning-based profiling side-channel analysis has been many proposed. Deep learning-based profiling analysis is a technique that trains the relationship between the side-channel information and the intermediate values to the neural network, then finds the secret key of the attack device using the trained neural network. Recently, cross-device profiling side channel analysis was proposed to consider the realistic deep learning-based profiling side channel analysis scenarios. However, it has a limitation in that attack performance is lowered if the profiling device and the attack device have not the same chips. In this paper, an environment in which the profiling device and the attack device have not the same chips is defined as the different-device, and a novel deep learning-based profiling side-channel analysis on different-device is proposed. Also, MCNN is used to well extract the characteristic of each data. We experimented with the six different boards to verify the attack performance of the proposed method; as a result, when the proposed method was used, the minimum number of attack traces was reduced by up to 25 times compared to without the proposed method.

• Journal of Digital Convergence
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• v.19 no.8
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• pp.319-325
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• 2021

With the advancement of wireless technology and the rapid growth of the infrastructure of mobile communication technology, systems applying AI-based platforms are drawing attention from users. In particular, the system that understands users' tastes and interests and recommends preferred items is applied to advanced e-commerce customized services and smart homes. However, there is a problem that these recommendation systems are difficult to reflect in real time the preferences of various users for tastes and interests. In this research, we propose a Fuzzy-AHP-based movies recommendation system using the Gated Recurrent Unit (GRU) language model to address a problem. In this system, we apply Fuzzy-AHP to reflect users' tastes or interests in real time. We also apply GRU language model-based models to analyze the public interest and the content of the film to recommend movies similar to the user's preferred factors. To validate the performance of this recommendation system, we measured the suitability of the learning model using scraping data used in the learning module, and measured the rate of learning performance by comparing the Long Short-Term Memory (LSTM) language model with the learning time per epoch. The results show that the average cross-validation index of the learning model in this work is suitable at 94.8% and that the learning performance rate outperforms the LSTM language model.

• Korean Journal of Environment and Ecology
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• v.36 no.5
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• pp.535-549
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• 2022

Damage to forests, such as broken or falling trees, has increased due to the increased intensity and frequency of abnormal climate events, such as strong winds and heavy rains. However, it is difficult to respond to them in advance based on prediction since structural defects such as cavities and bumps inside trees are difficult to identify with a visual inspection. Non-invasive sonic tomography (SoT) is a method of estimating internal defects while minimizing physical damage to trees. Although SoT is effective in diagnosing internal defects, its accuracy varies depending on the species. Therefore, it is necessary to analyze the reliability of its measurement results before applying it in the field. In this study, we measured internal defects in wood by cross-applying destructive resistance micro drilling on old Pinus densifloraSiebold & Zucc. and Ginkgo bilobaL., which are representative tree species in Korea, to verify the reliability of SoT and compared the evaluation results. The t-test for the mean values of the defect measurement between the two groups showed no statistically significant difference in pine trees and some difference in ginkgo trees. Linear regression analysis results showed a positive correlation with an increase in defects in SoT images when the defects in the drill resistance graph increased in both species.

• Journal of the Korean Geotechnical Society
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• v.38 no.5
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• pp.35-46
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• 2022

The in-situ resource utilization (ISRU) for sustainable lunar surface and deep space explorations has recently gained attention. Also, research on the development of construction material preparation technology using lunar regolith is in progress. Microwave sintering technology for construction material preparation does not require a binder and is energy efficient. This study applies microwave sintering technology to KLS-1, a Korean lunar simulant. It is crucial to secure the homogeneity to produce a sintered specimen for construction material. Therefore, understanding the interactions between microwaves, cavities, and raw materials is required. Using a numerical model in terms of efficient assessment of several cases and establishment of equipment operating conditions is a very efficient approach. Therefore, this study also proposes and verifies a coupled electric-thermal numerical model through cross-validation and comparison with experimental results. The numerical model proposed in this study will be used to present an efficient method for producing construction material using microwave sintering technology.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.3
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• pp.74-99
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• 2022

Surface ozone is produced by photochemical reactions of nitrogen oxides(NOx) and volatile organic compounds(VOCs) emitted from vehicles and industrial sites, adversely affecting vegetation and the human body. In South Korea, ozone is monitored in real-time at stations(i.e., point measurements), but it is difficult to monitor and analyze its continuous spatial distribution. In this study, surface ozone concentrations were interpolated to have a spatial resolution of 1.5km every hour using the stacking ensemble technique, followed by a 5-fold cross-validation. Base models for the stacking ensemble were cokriging, multi-linear regression(MLR), random forest(RF), and support vector regression(SVR), while MLR was used as the meta model, having all base model results as additional input variables. The results showed that the stacking ensemble model yielded the better performance than the individual base models, resulting in an averaged R of 0.76 and RMSE of 0.0065ppm during the study period of 2020. The surface ozone concentration distribution generated by the stacking ensemble model had a wider range with a spatial pattern similar with terrain and urbanization variables, compared to those by the base models. Not only should the proposed model be capable of producing the hourly spatial distribution of ozone, but it should also be highly applicable for calculating the daily maximum 8-hour ozone concentrations.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.6
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• pp.93-101
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• 2022

RC Slab bridges in Korea account for more than 70% of the total bridges for more than 20 years of service. As the number of aging structures increases, the importance of safety diagnosis and maintenance of structures increases. For highway bridges, cracks are a main cause of deck deterioration, which is very closely related to the decrease in bridge durability and service life. In addition, the damage rate of expansion joints and bearings accounts for approximately 73% higher than that of major members. Therefore, this study defined damage scenarios combined with devices damages and deck deterioration. The stress distribution and maximum stress on the deck were then evaluated using design vehicle load and daily temperature gradient for single and combined damage scenarios. Furthermore, this study performed damage-spread analysis and predicted condition ratings according to a deck deterioration model generated from the inspection and diagnosis history data of cracks. The heterogeneous damages combined with the member damages of expansion joints and bearings increased the rate of crack area and damage spread, which accelerated the time to reach the condition rating of C. Therefore, damage to bridge members requires proper and prompt repair and replacement, and otherwise it can cause the damage to bridge deck and the spread of the damage.

• Journal of Life Science
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• v.33 no.8
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• pp.639-650
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• 2023

The levels of harmful components in aerosols from heated tobacco products (HTPs) have been reported to be significantly lower than in cigarette smoke. However, it remains unclear whether the use of HTPs can mitigate the cardiovascular risks associated with cigarette smoking (CS). The objective of this study was to investigate the effects of a short-term switch from CS to HTP use on cardiac autonomic regulation (CAR). Seven healthy male smokers completed an open-label, randomized, cross-over trial consisting of five days of CS, use of three different HTPs (IQOS use, IQ; lil SOLID use, LS; lil HYBRID use, LH), or non-smoking (NS). Each session was separated by a one-week washout period, and levels of exhaled carbon monoxide (CO) and carboxyhemoglobin (COHb), systolic (SBP) and diastolic blood pressure (DBP), and heart rate variability (HRV) reflecting CAR were assessed before use of the product assigned to each session and at 24, 48, 72, 96, and 120 hr after use. Levels of exhaled CO and COHb were statistically significantly reduced only during NS. There were no statistical changes in SBP and DBP within any session. However, in HRV spectral analysis, log-transformed high frequency (lnHF) increased statistically significantly in IQ, LS, and NS, respectively. Normalized HF (HFnu) was significantly increased in NS and LH, respectively. lnHF and HFnu showed significant interaction effects. The findings of this study suggest that a short-term switch to HTPs instead of CS may lead to different distribution patterns of CAR, primarily driven by enhanced cardiac vagal tone.

• Journal of Technology Innovation
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• v.30 no.1
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• pp.57-104
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• 2022

The government has supported the innovation of private firms by intervening the market for various purposes, such as preventing market failure, alleviating information asymmetry, and allocating resources efficiently. Although the government's R&D budget increased rapidly in the 2000s, it is not clear whether the government intervention has made desirable impact on the market. To address this, the current study attempts to explore this issue by doing a systematic literature review on foreign and domestic papers in an integrated way. In total, 168 studies are analyzed using contents analysis approach and various lens, such as policy additionality, policy tools, firm size, unit of analysis, data and method, are adopted for analysis. Overlapping policy target, time lag between government intervention and policy effects, non-linearity of financial supports, interference between different polices, and out-dated R&D tax incentive system are reported as factors hampering the effect of the government intervention. Many policy prescriptions, such as program evaluation indices reflecting behavioral additionality, an introduction of policy mix and evidence-based policy using machine learning, are suggested to improve these hurdles.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.448-448
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• 2021

최근 급격한 기후변화와 도시화 및 산업화로 인한 지류하천에서의 수량과 수질의 변동은 생물 다양성 감소와 수생태계 건강성 저하에 큰 영향을 미치고 있다. 효율적인 수생태 관리를 위해서는 지속적인 유량, 수질, 그리고 수생태 모니터링을 통한 데이터 축적과 더불어 면밀한 상관 분석을 통해 수생태계 건강성의 악화 원인을 규명해야 할 필요가 있다. 그러나 수많은 지류하천을 대상으로 한 지속적인 모니터링은 현실적으로 어려움이 있으며, 수생태계의 특성 상 단일 영향 인자만으로 수생태계의 건강성 변화와의 관계를 정확히 파악하는데 한계가 있다. 따라서 지류하천에서의 유량 및 수질의 시공간적인 변동성과 다양한 영향 인자를 고려하여 수생태계의 건강성을 효율적으로 예측할 수 있는 기술이 필요하다. 이에 본 연구에서는 경험적 데이터 기반의 머신러닝 모델 구축을 통해 미계측 하천에서의 수생태계 건강성 지수(BMI, TDI, FAI)의 등급(A to E)을 예측하고자 하였다. 머신러닝 모델은 학습 데이터셋의 양과 질에 따라 성능이 크게 달라질 수 있으며, 학습 데이터셋의 분포가 불균형적일 경우 과적합 또는 과소적합 문제가 발생할 수 있다. 이를 보완하고자 본 연구에서는 실제 측정망 데이터셋을 바탕으로 생성적 적대 신경망 GAN(Generative Adversarial Network) 알고리즘을 통해 머신러닝 모델 학습에 필요한 추가 데이터셋(유량, 수질, 기상, 수생태 등급)을 확보하였다. 머신러닝 모델의 성능은 5차 교차검증 과정을 통해 평가하였으며, GAN 데이터셋의 정확도는 실제 측정망 데이터셋의 정규분포와의 비교 분석을 통해 평가하였다. 최종적으로 SWAT(Soil and Water Assessment Tool) 모형을 통해 예측 된 미계측 하천에서의 데이터셋을 머신러닝 모델의 검증 자료로 사용하여 수생태계 건강성 등급 예측 정확도를 평가하였다. 본 연구에서의 GAN에 의해 강화된 머신러닝 모델은 수질 및 수생태 관리가 필요한 우심 지류하천 선정과 구조적/비구조적 최적관리기법에 따른 수생태계 건강성 개선 효과를 평가하는데 활용될 수 있을 것이다. 또한 이를 통해 예측된 미계측 하천에서의 수생태계 건강성 등급 자료는 수량-수질-수생태를 유기적으로 연계한 통합 물관리 정책을 수립하는데 기초자료로 활용될 수 있을 것이라 사료된다.

• Journal of Practical Agriculture & Fisheries Research
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• v.20 no.2
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• pp.105-114
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• 2018

The aim of this study was to evaluate the change of phenolic compounds after phosphite treatment on Cheorwon onion. Onion is a perennial plant belonging to the lily family. It is native to Persia of Southwest Asia. It is widely cultivated in the temperate regions of the world. Onion is a good name for the 'Okchong' to drop blood cholesterol and cardiovascular blood flow to increase the prevention of adult diseases. Cheorwon area is inland, but it has high continental climate due to its high altitude. Therefore it is said that the onion cultivated in this region has higher sugar content and higher taste than onion grown in the southern region. Phosphorus components are particularly important ingredients for promoting muscle development. However, if the phosphoric acid content of the soil part is maintained to a large extent until the harvest, the competition of the nutrients tends to cause decay of the root part. Therefore, it is important to improve the quality and shelf life of onion by inducing nutrient balance by applying foliar fertilization method on the reducing phosphorus at harvest time. In this study, acidity was controlled by diluting phosphorous acid(H₃PO₃) and potassium hydroxide(KOH), followed by leaf surface treatment with phosphite on onion. In this study, the concentration of phosphite was diluted to 500, 1,000, 1,500ppm and sprayed three times over the onion leaves in May 2018 using an atomizer and harvested at the end of June, and the phenolic compounds were analyzed by HPLC. As a result, the content of quercetin, one of the important substances in onion, was phosphite 500ppm(179.70㎍/g), 1,000(150.27), 1,500(105.95). The contents of caffeic acid, p-coumaric acid, ferulic acid, rutin, kaempferol, and sugar content were higher in the treatments than in the control. Therefore, the phosphite does not have a great influence on the growth, but it may play a role as a method of achieving balance with nitrogen in the rainy season by supplying the role of the material catalyst and the water soluble phosphoric acid and the potassium in the influence of the material change.