• East Asian mathematical journal
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• 제39권1호
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• pp.51-63
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• 2023

We establish the interior L^q regularity estimates for spatial gradient of weak solutions to nonlinear parabolic equations with the inhomogeneity which is given by the divergence and nondivergence data.

• Journal of Ecology and Environment
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• 제47권4호
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• pp.272-281
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• 2023

Ecosystem observatories are burgeoning globally in an endeavour to detect national and global scale trends in the state of biodiversity and ecosystems in an era of rapid environmental change. In this paper we highlight the additional importance of regional scale outcomes of such infrastructure, through an introduction to the Great Western Woodlands TERN (Terrestrial Ecosystem Research Network) SuperSite, and key findings from three gradient plot networks that are part of this infrastructure. The SuperSite was established in 2012 in the 160,000 km² Great Western Woodlands region, in a collaboration involving 12 organisations. This region is globally significant for its largely intact, diverse landscapes, including the world's largest Mediterranean-climate woodlands and highly diverse sandplain shrublands. The dominant woodland eucalypts are fire-sensitive, requiring hundreds of years to regrow after fire. Old-growth woodlands are highly valued by Indigenous and non-Indigenous communities, and managing impacts of climate change and the increasing extent of intense fires are key regional management challenges. Like other TERN SuperSites, the Great Western Woodlands TERN SuperSite includes a core eddy-covariance flux tower measuring exchanges of carbon, water and energy between the vegetation and atmosphere, along with additional environmental and biodiversity monitoring around the tower. The broader SuperSite incorporates three gradient plot networks. Two of these represent aridity gradients, in sandplains and woodlands, informing regional climate adaptation and biodiversity management by characterising biodiversity turnover along spatial climate gradients and acting as sentinels for ecosystem change over time. For example, the sandplains transect has demonstrated extremely high spatial turnover rates in plant species, that challenge traditional approaches to biodiversity conservation. The third gradient plot network represents a 400-year fire-age gradient in Eucalyptus salubris woodlands. It has enabled characterisation of post-fire recovery of vegetation, birds and invertebrates over multi-century timeframes, and provided tools that are directly informing management to reduce stand-replacing fires in eucalypt woodlands. By building regional partnerships and applying globally or nationally consistent methodologies to regional scale questions, ecological observatories have the power not only to detect national and global scale trends in biodiversity and ecosystems, but to directly inform environmental decisions that are critical at regional scales.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2009년도 학술발표회 초록집
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• pp.1260-1264
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• 2009

Precipitation time series is a mixture of complicate fluctuation and changes. The monthly precipitation data of 61 stations during 36 years (1973-2008) in Korea are comprehensively analyzed using the EOFs technique and CSEOFs technique respectively. The main motivation for employing this technique in the present study is to investigate the physical processes associated with the evolution of the precipitation from observation data. The twenty-five leading EOF modes account for 98.05% of the total monthly variance, and the first two modes account for 83.68% of total variation. The first mode exhibits traditional spatial pattern with annual cycle of corresponding PC time series and second mode shows strong North South gradient. In CSEOF analysis, the twenty-five leading CSEOF modes account for 98.58% of the total monthly variance, and the first two modes account for 78.69% of total variation, these first two patterns' spatial distribution show monthly spatial variation. The corresponding mode's PC time series reveals the annual cycle on a monthly time scale and long-term fluctuation and first mode's PC time series shows increasing linear trend which represents that spatial and temporal variability of first mode pattern has strengthened. Compared with the EOFs analysis, the CSEOFs analysis preferably exhibits the spatial distribution and temporal evolution characteristics and variability of Korean historical precipitation.

• 생태와환경
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• 제43권2호
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• pp.199-211
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• 2010

The objectives for this study were to evaluate spatial and temporal characteristics of water quality, based on long-term water quality monitoring data during 1993~2008. We found that physico-chemical and ecological conditions in the Daecheong Reservoir (DR) were modified by the construction of upper dam (i.e., Yongdam Reservoir). total phosphorus (TP), Secchi depth (SD), and chlorophyll-a (CHL) in the DR showed significant longitudinal decreases along the headwater-to-the downlake, indicating a large spatial variation, and this gradient was more intensified during the high-flow season (monsoon). Nutrient-rich water containing high nitrogen and phosphorus in the monsoon season (July~August) passed through the reservoir as a density current in the metalimnetic depth, and also high suspended solids increased in the metalimnetic depth, especially during the monsoon. According to the deviation analysis of Trophic State Index (TSI), >50% of TSI (CHL)-TSI (SD) and TSI (CHL)-TSI (TP) values were negatives, so that inorganic suspended solids (non-votatile solids) influenced the underwater light regime against phytoplankton growth. Also, ratios of CHL:TP after the dam construction evidently increased, compared to the values before the upper dam constructions, indicating a greater yield of phytoplankton in the unit phosphorus. Overall data showed that ecological and functional changes in Daecheong Reservoir occurred after the construction of upper dam (Yongdam Reservoir).

• 한국전자파학회논문지
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• 제9권5호
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• pp.640-647
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• 1998

다중 분해능 웨이블릿 해석법을 마이크로스트립 패치 안테나의 산란해석에 적용하였다. 다충구조에 대한 스펙 트럼 영역 그린 함수(spectral domain Green's dyad)의 특성올 공간-스펙트럼 영역 표현법을 이용하여 살펴보고, 스펙트럼 영역 웨이블릿을 주어진 문제에 적용하는 것이 유용함을 관찰하였다. 적분방정식에 모멘트법을 이 용하여 행렬방정식을 얻고, 그 풀이에 CG(conjugate gradient)법과 스펙트럼 영역 웨이블릿올 결합하여 효율 적으로 문제를 풀이할 수 있다. 단충구조 위에 놓인 정방형 패치에 대하여 기폰의 모멘트법 결과와 다충 분해능 웨이블릿 해석법올 적용한 결과를 비교하였다.

• 한국공간구조학회논문집
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• 제21권2호
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• pp.41-48
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• 2021

A smart tuned mass damper (TMD) is widely studied for seismic response reduction of various structures. Control algorithm is the most important factor for control performance of a smart TMD. This study used a Deep Deterministic Policy Gradient (DDPG) among reinforcement learning techniques to develop a control algorithm for a smart TMD. A magnetorheological (MR) damper was used to make the smart TMD. A single mass model with the smart TMD was employed to make a reinforcement learning environment. Time history analysis simulations of the example structure subject to artificial seismic load were performed in the reinforcement learning process. Critic of policy network and actor of value network for DDPG agent were constructed. The action of DDPG agent was selected as the command voltage sent to the MR damper. Reward for the DDPG action was calculated by using displacement and velocity responses of the main mass. Groundhook control algorithm was used as a comparative control algorithm. After 10,000 episode training of the DDPG agent model with proper hyper-parameters, the semi-active control algorithm for control of seismic responses of the example structure with the smart TMD was developed. The simulation results presented that the developed DDPG model can provide effective control algorithms for smart TMD for reduction of seismic responses.

• 한국공간구조학회논문집
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• 제23권3호
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• pp.87-94
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• 2023

Chloride is one of the most common threats to reinforced concrete (RC) durability. Alkaline environment of concrete makes a passive layer on the surface of reinforcement bars that prevents the bar from corrosion. However, when the chloride concentration amount at the reinforcement bar reaches a certain level, deterioration of the passive protection layer occurs, causing corrosion and ultimately reducing the structure's safety and durability. Therefore, understanding the chloride diffusion and its prediction are important to evaluate the safety and durability of RC structure. In this study, the chloride diffusion coefficient is predicted by machine learning techniques. Various machine learning techniques such as multiple linear regression, decision tree, random forest, support vector machine, artificial neural networks, extreme gradient boosting annd k-nearest neighbor were used and accuracy of there models were compared. In order to evaluate the accuracy, root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R2) were used as prediction performance indices. The k-fold cross-validation procedure was used to estimate the performance of machine learning models when making predictions on data not used during training. Grid search was applied to hyperparameter optimization. It has been shown from numerical simulation that ensemble learning methods such as random forest and extreme gradient boosting successfully predicted the chloride diffusion coefficient and artificial neural networks also provided accurate result.

• 한국공간구조학회논문집
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• 제23권4호
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• pp.81-88
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• 2023

Numerous factors contribute to the deterioration of reinforced concrete structures. Elevated temperatures significantly alter the composition of the concrete ingredients, consequently diminishing the concrete's strength properties. With the escalation of global CO2 levels, the carbonation of concrete structures has emerged as a critical challenge, substantially affecting concrete durability research. Assessing and predicting concrete degradation due to thermal effects and carbonation are crucial yet intricate tasks. To address this, multiple prediction models for concrete carbonation and compressive strength under thermal impact have been developed. This study employs seven machine learning algorithms-specifically, multiple linear regression, decision trees, random forest, support vector machines, k-nearest neighbors, artificial neural networks, and extreme gradient boosting algorithms-to formulate predictive models for concrete carbonation and thermal impact. Two distinct datasets, derived from reported experimental studies, were utilized for training these predictive models. Performance evaluation relied on metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analytical outcomes demonstrate that neural networks and extreme gradient boosting algorithms outshine the remaining five machine learning approaches, showcasing outstanding predictive performance for concrete carbonation and thermal effect modeling.

• 정보처리학회논문지B
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• 제17B권4호
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• pp.303-308
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• 2010

본 논문에서는 기울기 벡터장과 조건부 엔트로피를 결합한 의료영상 정합 방법을 제안한다. 정합 방법은 조건부 확률의 엔트로피에 기반한 측도를 수행한다. 먼저 공간적 정보를 얻기 위해 윤곽선 정보의 방향을 제공하는 기울기 정보인 기울기 벡터장을 계산한다. 다음으로 주어진 두 영상에서 픽셀의 밝기정보와 에지정보를 결합하여 조인트 히스토그램을 계산하여 조건부 엔트로피를 구하고, 이것을 두 영상의 정합측도로 사용한다. 제안된 방법의 성능평가를 위해 자기공명 영상과 변환된 컴퓨터단층촬영 영상에 기존 방법인 상호정보기반의 측도, 조건부 엔트로피만을 사용한 측도와 비교 실험을 수행한다. 실험결과로부터 제안한 방법이 기존의 최적화 방법들 보다 더 빠르고 정확한 정합임을 알 수 있다.

• 자원환경지질
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• 제35권2호
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• pp.163-170
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• 2002

본 연구에서는 남한 지역의 암상과 지온경사율 상관관계를 GIS를 이용하여 분석하였다 .이러한 분석을 위해 352개의 시추공 온도검층자료가 공간 Layer로 구축되었고, 이러한 공간 Layer 및 1:1,000,000 축적의 지질도 공간 Layer를 중첩하여 지온경사율 및 암상별, 지질시대벽 관계를 파악하였다. 그 결과 남한 지역의 평균 지온경사율 값은 29.34$^{\circ}C$/km이었다. 지질시대별로는 신생대 지층이 39.7$0^{\circ}C$/km 중생대 지층이 30.63$^{\circ}C$/km 고생대 지층이 22.32$^{\circ}C$/km 원생대 지층이 23.15$^{\circ}C$/km시생대 지층이 24.34$^{\circ}C$/km의 지온평사율 값을 가졌다. 암상 종류별로는 심성암이 33.96$^{\circ}C$/km, 퇴적암이 24.78$^{\circ}C$/km 퇴적암과 화산암이 26.85$^{\circ}C$/km 지온경사율 값을 가졌다. 이러한 결과는 지열 및 온천개발 시 기초자료로 사용될 수 있다.