• Journal of Environmental Impact Assessment
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• v.27 no.6
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• pp.582-594
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• 2018

In this study, the vertical and horizontal flow wetlands were combined in series to create conditions for flow in the exhalation and anaerobic state with the aim of monitoring the variability and reduction of dissolved organic matterin the bio-reactive artificial wetlands, and the performance assessment was conducted as acrylic reaction groups by designing artificial wetlands that filled the functionalresiduals. In case of artificial wetlands in vertical and horizontal planes, the concentration of dissolved oxygen (DO) in the reaction tank was measured as 2.7 mg/L in the vertical flow wetlands under exhalation, and N.D. in the horizontal flow artificial wetlands under anaerobic conditions. The test was carried out by changing the operation time to 140 min, 80 min, and 60 min. The test was conducted with the same natural operation time of 20 min depending on the operation time. All hours of operation were shown to be due to microbial activity. In 3D-EEM, it was found that the longer the driving time was taken, the more reduction the organic compounds in the areas of insoluble human resources, III and V. Further research on the mechanism analysis of future reduction effects is expected to be carried out, but the findings are expected to contribute to the development of technologies for reducing obfuscated substances using artificial wetlands in the future.

• Journal of Korean Society of Steel Construction
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• v.17 no.6 s.79
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• pp.661-672
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• 2005

For the efficient stochastic optimization of steel structures for which a large number of analyses is required, artificial neural networks,which have emerged as a powerful tool that could have been used to replace time-consuming procedures in many scientific or engineering applications, are applied. They are utilized for the solution of the equilibrium equations resulting from the application of the finite element method in connection with the reanalysis type of problem, for which a large number of finite element analyses are required in this study. As such, the use of artificial neural networks to predict finite element analysis outputs simplifies and facilitates the performance of the stochastic optimal design of structural systems where a trained neural network is used to replace the structural reanalysis phase. Moreover, to improve efficiency of used artificial neural networks, genetic algorithm is utilized. The stochastic optimizer used in this study is an algorithm based on the evolution theory. The efficiency of the proposed procedure is examined in problems with both volume (weight) functions and real-world cost functions

• Journal of Korean Society of Steel Construction
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• v.16 no.4 s.71
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• pp.443-452
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• 2004

Recently, the Artificial Neural Network(ANN) which can organize complex non-linear problems by effectively applying the parallel computational model that is similar to the human brain, was adopted in the wide department of technology and resulted in many successful applications. In this study, a more appropriate formal method is suggested for the preliminary structural design stage controlled merely by the designer's experience and intuition. To do so, this study proposes a multi-level ANN according to the general progressive structural design procedure, using Back-Propagation Algorithm (BP) and Genetic Algorithm (GA) for the ANN learning. The preliminary structural design of cable-stayed bridges was applied to illustrate the applicability of the study formulated as stated above, and the results of two different learning methods were compared.

• Korean Journal of Agricultural Science
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• v.24 no.2
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• pp.182-193
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• 1997

The normal cement concrete is widely used material to build the construction recently, but it has a fault to increase the dead load on account of its unit weight is large compared with strength. Therefore, many engineers are continuously searching for new materials of construction to provide greater performance at lower density. The main purpose of the work described in this paper were to establish the physical and mechanical properties of synthetic lightweight aggregate concrete using perlite on fine aggregate and expanded clay, pumice stone on coarse aggregate. The test results of this study are summarized that the water-cement ratio was shown 47% using expanded clay, 56% using pumice stone on coarse aggregate, unit weight was shown $l,622kgf/m^3$ using expanded clay, $l,596kgf/m^3$ using pumice stone on coarse aggregate, and the absorption ratio was shown same as 17%. The compressive strength was shown more than $228kgf/cm^2$, tensile and bending strength was more than $27kgf/cm^2$, $58kgf/cm^2$ at all types, and rebound number with schmidt hammer was increased with increase of compressive strength. The static modulus was $1.12{\times}10^5kgf/cm^2$ using expanded clay, $1.09{\times}10^5kgf/cm^2$ using pumice stone on coarse aggregate, and stress-strain curves were shown that increased with increase of stress, and the strain on the maximum stress was shown identical with $2.0{\times}10^{-3}$, approximately.

• Smart Media Journal
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• v.12 no.10
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• pp.63-70
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• 2023

Recently, energy consumption for heating costs, which is 35% of smart farm energy costs, has increased, requiring energy consumption efficiency, and the importance of new and renewable energy is increasing due to concerns about the realization of electricity bills. Renewable energy belongs to hydropower, wind, and solar power, of which solar energy is a power generation technology that converts it into electrical energy, and this technology has less impact on the environment and is simple to maintain. In this study, based on the greenhouse heat storage tank and heat pump data, the factors that affect the heat storage tank are selected and a heat storage tank supply temperature prediction model is developed. It is predicted using Long Short-Term Memory (LSTM), which is effective for time series data analysis and prediction, and XGBoost model, which is superior to other ensemble learning techniques. By predicting the temperature of the heat pump heat storage tank, energy consumption may be optimized and system operation may be optimized. In addition, we intend to link it to the smart farm energy integrated operation system, such as reducing heating and cooling costs and improving the energy independence of farmers due to the use of solar power. By managing the supply of waste heat energy through the platform and deriving the maximum heating load and energy values required for crop growth by season and time, an optimal energy management plan is derived based on this.

• The Korean Journal of Ecology
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• v.25 no.5
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• pp.275-282
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• 2002

Preference of food plants of wild birds was shown in the order of Pinus densiflora, Celtis sinensis, Celastrus orbiculatus, Rosa multiflora, Taxus cuspidata, and Euonymus japonica, etc. The wild birds preferred the animal food(92.2%) rather than the vegetable food(32.7%) in breeding season(May through June), but preferred both animal and vegetable foods in non-breeding season(November through December). The rates utilzing artficial nest were 77.5%, 50.3%, 44.1% and 42.2% in Nosan, Sanho, Chinju-castle and Bibong parks, respectively. The wild birds used more the 3cm hole than 5cm hole.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.285-285
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• 2020

본 연구에서는 국내 시설농업단지의 수리지질 특성을 고려한 개념 모델을 설정하여 수리전도도와 이격거리(주입정과 양수정 사이의 거리)에 대한 민감도 분석을 수행하였다. 개념 모델에는 자연적 특성(지형과 지질, 강수량, 수리전도도 등)과 인공적 특성(주입정과 양수정의 이격거리, 주입량과 양수량 등)이 입력되었으며, 민감도 분석은 수리전도도(10^-1 cm/sec, 10^-2 cm/sec, 10^-3 cm/sec, 10^-4 cm/sec)와 이격거리(10 m, 50 m, 100 m)를 조합한 12개의 시나리오로 수행하였다. 양수정의 하류부에 설정된 관측정의 지하수위 강하량은 수리전도도가 감소하고 이격거리가 멀어질수록 증가하였다. 동일한 이격거리에서 수리전도도에 의한 지하수위 강하량의 회귀분석을 통해 인공함양 대수층의 지하수위 변동은 수리전도도에 의해 지배적인 영향을 받음을 알 수 있었다. 인공함양 대수층의 수리전도도가 10^-2 cm/sec 이상인 조건에서는 주입정과 양수정의 이격거리에 따른 지하수위의 영향반경은 20 m 이내이었지만, 수리전도도가 10^-3 cm/sec 이하인 조건에서는 이격거리가 멀어질수록 지하수위의 영향반경이 급격하게 증가함을 확인하였다.

• Smart Media Journal
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• v.13 no.3
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• pp.45-52
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• 2024

In today's growing threat environment, rapid and effective detection and response to security events is essential. To solve these problems, many companies and organizations respond to security threats by introducing security control systems. However, existing security control systems are experiencing difficulties due to the complexity and diverse characteristics of security events. In this study, we propose an automated integrated security control system model based on artificial intelligence. It is based on deep learning, an artificial intelligence technology, and provides effective detection and processing functions for various security events. To this end, the model applies various artificial intelligence algorithms and machine learning methods to overcome the limitations of existing security control systems. The proposed model reduces the operator's workload, ensures efficient operation, and supports rapid response to security threats.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.536-540
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• 1996

경험과 학습을 바탕으로 새로운 상황에 대처하는 인간의 신경계에서의 신경세포들의 상호작용을 규명하는 일은 많은 과학자들을 매료시켜 왔다. 이와 함께, 생물학적인 신경계를 닮은 인공적인 신경망을 구축하여 감지하고, 인식하고, 구별하고, 판단하는 일에 이용하고자 하는 노력도 끊임없이 진행되어 왔다. 인공 신경망의 구성은 전적으로 많은 경우의 수에 대한 테스트에 의존하게 되는 데, 이 경우 시간도 대단히 많이 소요 될 뿐더러 체계적으로 가장 좋아 보이는 신경망 모델에 도달하였는 지도 확실치 않다. 따라서 , 본 논문에서는 실험계획법을 다층 퍼셉트론 신경망의구조설계에 적용하여 적은 실험횟수로 적합한 신경망 모델을 구성할 수 있음을 검증하고, 실제사례에 적용하여 그 유용성을 제시하고자 한다.

• Proceedings of the Korean Vacuum Society Conference
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• 2013.02a
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• pp.147-147
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• 2013

인공위성이 임무를 수행하는 우주공간은 고진공 환경과 태양 복사열에 의한 고온 환경 및 극저온이 반복되는 가혹한 환경으로, 위성체는 이러한 가혹한 우주환경의 영향으로 인해 주요부품의 기능장애가 초래되기도 하며 이는 결국 임무의 실패로 이어지도 한다. 따라서 10E-06 torr 이하의 고진공과 $-180^{\circ}C$의 극저온 환경으로 일컬어지는 우주환경을 지상에서 모사하여 위성체의 안정성 및 신뢰성을 시험하기 위해서 열진공 시험장비를 이용한 열진공시험을 수행한다. 한국항공우주연구원에서는 인공위성의 탑재체인 광학카메라의 국산화 개발을 위하여 우주공간의 고진공과 극저온 상태를 모사할 수 있는 ${\varphi}4m{\times}L10m$ 규모의 광학탑재체 전용 열진 공챔버를 국산화 개발하여 사용하고 있다. 탑재체 진공시험은 진공환경의 조성과 함께 외부진동을 완벽하게 차단하는 것이 매우 중요하다. 본 논문에서는 한국항공우주연구원에서 보유한 광학탐재체용 진공챔버에서 진공 유지와 진동 차단을 동시에 수행하고 있는 방법에 대해 살펴보고자 한다.