This study presents distribution of naturally occurring radioactive materials in groundwater in Jeju island. Radon (222Rn) and potassium (40K) concentrations were performed by using Liquid Scintillation Counter and Ion Chromatograph respectively. In addition, the activities of uranium and thorium nuclides were analyzed by Inductively Coupled Plasma Mass Spectroscopy. Groundwater samples were collected from 9 sites of water intake facilities for wide area supply in Jeju island from September 2022 to September 2023. The 40K concentrations of groundwater ranged between 0.050 and 0.400 Bq·L-1. The radon concentrations in groundwater were in the range of 0 to 60 Bq L-1, and there was no groundwater exceeding the range of 148 Bq L-1 proposed by the US EPA. The distribution of uranium and thorium in groundwater varied from 0 to 500 ng L-1 and 0 to 2.4 ng L-1, respectively. The concentrations of uranium did not exceed 30 μg L-1, thresholds indicated by the US EPA. By analyzing the concentrations of 40K, 222Rn, 238U and 232Th, the annual effective dose of residents can be assessed. The evaluated residents’ effective dose from natural radionuclides due to intake of drinking water is less than the recommended value of 100 μSv y-1. Consequently, this study indicates that the cancer risks of the residents in Jeju island from naturally occurring radioactive materials ingested with water is insignificant.

In order to apply indirect methods (such as scaling factors) to assess the radionuclide inventory of waste generated by nuclear power plants, it is essential to first evaluate the correlation coefficient between key radionuclides and those that are difficult to measure (DTM). The benchmark for the correlation coefficient (r) applied in indirect assessments is set at 0.6, and its significance can vary based on both its value and the size of the dataset. For instance, deriving a correlation coefficient using three data points versus utilizing a dataset with a hundred data points would yield different implications. This study addresses the variance in correlation coefficients based on data selection and presents a methodology for validating the significance of these coefficients. Additionally, we will discuss how these variances may impact the results of indirect assessments, such as scaling factor evaluations.

When an earthquake occurs, the severity of damage is determined by natural factors such as the magnitude of the earthquake, the epicenter distance, soil properties, and type of the structures in the affected area, as well as the socio-economic factors such as the population, disaster prevention measures, and economic power of the community. This study evaluated the direct economic loss due to building damage and the community’s recovery ability. Building damage was estimated using fragility functions due to the design earthquake by the seismic design code. The usage of the building was determined from the information in the building registrar. Direct economic loss was evaluated using the standard unit price and estimated building damage. The standard unit price was obtained from the Korean Real Estate Board. The community’s recovery capacity was calculated using nine indicators selected from regional statistical data. After appropriate normalization and factor analysis, the recovery ability score was calculated through relative evaluation with neighboring cities.

증산은 적정 관수 관리에 중요한 역할을 하므로 수분 스트레스에 취약한 토마토와 같은 작물의 관개 수요에 대한 지식이 필요하다. 관수량을 결정하는 한 가지 방법은 증산량을 측정하는 것인데, 이는 환경이나 생육 수준의 영향을 받는다. 본 연구는 분단위 데이터를 통해 수학적 모델과 딥러닝 모델을 활용하여 토마토의 증발량을 추정하 고 적합한 모델을 찾는 것을 목표로 한다. 라이시미터 데이터는 1분 간격으로 배지무게 변화를 측정함으로써 증산 량을 직접 측정했다. 피어슨 상관관계는 관찰된 환경 변수가 작물 증산과 유의미한 상관관계가 있음을 보여주었다. 온실온도와 태양복사는 증산량과 양의 상관관계를 보인 반면, 상대습도는 음의 상관관계를 보였다. 다중 선형 회귀 (MLR), 다항 회귀 모델, 인공 신경망(ANN), Long short-term memory(LSTM), Gated Recurrent Unit(GRU) 모델을 구 축하고 정확도를 비교했다. 모든 모델은 테스트 데이터 세트에서 0.770－0.948 범위의 R2 값과 0.495mm/min－ 1.038mm/min의 RMSE로 증산을 잠재적으로 추정하였다. 딥러닝 모델은 수학적 모델보다 성능이 뛰어났다. GRU 는 0.948의 R2 및 0.495mm/min의 RMSE로 테스트 데이터에서 최고의 성능을 보여주었다. LSTM과 ANN은 R2 값이 각각 0.946과 0.944, RMSE가 각각 0.504m/min과 0.511로 그 뒤를 이었다. GRU 모델은 단기 예측에서 우수한 성능 을 보였고 LSTM은 장기 예측에서 우수한 성능을 보였지만 대규모 데이터 셋을 사용한 추가 검증이 필요하다. FAO56 Penman-Monteith(PM) 방정식과 비교하여 PM은 MLR 및 다항식 모델 2차 및 3차보다 RMSE가 0.598mm/min으로 낮지만 분단위 증산의 변동성을 포착하는 데 있어 모든 모델 중에서 가장 성능이 낮다. 따라서 본 연구 결과는 온실 내 토마토 증산을 단기적으로 추정하기 위해 GRU 및 LSTM 모델을 권장한다.

The blockage rate for three kinds of nets commonly used in agricultural facilities was assessed by using the image acquisition and its relevant processing. By using both empirical relations presented by Idel’chik and Richards and Robinson, and the blockage rate obtained from the image processing, the pressure drop through the nets was predicted and also compared with wind tunnel experiment results. The results of the study showed that the blockage rate of the net was discriminated according to such factors as the magnitude of nets, the existence of inside threads, the thickness and number of threads. In addition, the blockage rate for the incident angle of 0° when the wind blew at the front had the range of 0.22－0.29 (0.22－0.32 when considering whole incident angles from 0° to 45° by 15°). For the nets with the blockage rate of about 30% or below, the prediction by the empirical relations of by Idel’chik and Richards and Robinson showed a little higher pressure drop overall than that of the wind tunnel test, but the use of the empirical relations and the blockage rate could be thought of as providing effectively meaningful guidelines for the safe design of agricultural facilities including nets because the wind tunnel test has been tedious and expensive. Further research and potential application on the prediction technique of the pressure drop, regarding both a subtle deformation by the wind and manufacturing methods with regard to the level of knots and the existence of inside threads, needs to be done for the nets with higher blockage rate.

PURPOSES : In this study, a model was developed to estimate the concentrations of particulate matter (PM2.5 and PM10) in expressway tunnel sections. METHODS : A statistical model was constructed by collecting data on particulate matter (PM2.5 and PM10), weather, environment, and traffic volume in the tunnel section. The model was developed after accurately analyzing the factors influencing the PM concentration. RESULTS : A machine learning-based PM concentration estimation model was developed. Three models, namely linear regression, convolutional neural network, and random forest models, were compared, and the random forest model was proposed as the best model. CONCLUSIONS : The evaluation revealed that the random forest model displayed the least error in the concentration estimation model for (PM2.5 and PM10) in all tunnel section cases. In addition, a practical application plan for the model developed in this study is proposed.

PURPOSES : In this study, energy-consuming processes in asphalt plants were evaluated, and the drying and mixing processes were characterized using a thermal equilibrium equation-based model to quantitatively estimate the amount of energy consumed during the production of mixtures in asphalt concrete plants. METHODS : An energy consumption model based on the thermal equilibrium equation was used to estimate the energy consumption of the aggregate drying process that consumes the maximum energy; the energy consumed for material transportation, storage, and operation of other facilities was cited from the literature. The results were compared with the actual results obtained for recycled hot asphalt mixtures and recycled warm mix asphalt mixtures, and a sensitivity analysis was performed by varying the conditions. RESULTS : An analysis of the main processes required to produce asphalt mixtures showed that the water content had the largest impact on energy consumption (approximately 80%). This quantitatively supports the opinion of field practitioners that maximum energy is consumed during aggregate drying. Although some discrepancies were observed, the results were found to be reasonable and within the range of typical measurements. CONCLUSIONS : The thermal energy consumption estimation model provides consistent results that reflect the characteristics of the mixture and can be used to derive the thermal energy consumption rates for individual materials, such as aggregates and binders. This can be used to identify the priorities for process optimization within a plant.

PURPOSES : Previously, the expansion state of the concrete pavement in which AAR occurred could not be determined. Because the current situation has not been evaluated, it has been difficult to prepare an appropriate response. In this study, a method for calculating the expansion amount of concrete pavement using the stiffness damage test (SDT) is proposed. METHODS : The SDT method was examined through a literature review. For the laboratory tests, specimens that generated AAR were produced based on the mix design (2018) of the Korea Expressway Corporation. SDT was used to calculate various mechanical properties, and their correlation with the expansion amount was reviewed. RESULTS : Using the SDT, various mechanical properties(elastic modulus, hysteresis area, plastic deformation, plastic deformation index, stiffness damage index, and nonlinear index) were calculated based on the expansion rate of the AAR. The elastic modulus was evaluated as the best predictor of the expansion rate. Thus, if the elastic modulus is calculated using SDT, a prediction equation can be used to calculate the amount of AAR expansion. This equation will need to be supplemented by further research. CONCLUSIONS : SDT was used to confirm that the expansion state due to the AAR of the concrete pavement could be indirectly evaluated. Among the mechanical properties related to SDT, the elastic modulus was found to be the most suitable for predicting the amount of expansion.

본 연구는 난대상록수종인 종가시나무에 대한 수간곡선식의 도출과 재적표의 작성 및 탄소배출계수를 이용하여 탄소저장, 흡수량을 추정하고자 수행하였다. 분석에 사용된 공시목은 전남, 경남, 제주 등에서 수집된 468본이며, 수간형태를 도출하기 위하여 적용한 수간곡선 모형은 Kozak 모델이다. 이 모델의 적합도는 0.9452, 편의는 0.0807, 추정치 표준오차의 백분율은 1.7145, 평균절대편차는 1.2655로 각각 나타났다. 종가시나무의 개체목 재적은 Kozak 수간곡선 모델에 Smalian 재적식을 적용시켜 산출하였으며, 수고와 흉고직경급별로 재적표를 작성하였다. 그리고 붉가시나무 재적표와 이번에 만든 종가시나무 재적표를 서로 비교한 결과(t-test), 통계적으로 두 집단 간에 차이가 없는 것으로 나타났다. 따라서 이들 재적표는 두 수종 중 하나만 이용하거나, 둘을 하나로 통합하여도 문제가 없을 것으로 판단된다. 한편 종가시나무림의 탄소저장 및 흡수량은 산림바이오소재연 구소 시험림의 조사구를 대상으로 하였다. 이들 조사구를 대상으로 탄소저장 및 흡수량을 계산한 결과, 생육상태가 양호한 시험구(A)에서 탄소저장량 은 93.17 C ton/ha, 그리고 탄소흡수량은 13.14 CO2 ton/ha/yr 인 것으로 나타났다. 반면에 생육이 저조한 시험구(B)의 탄소 저장 및 흡수량은 양호한 시험구보다 약 1/3 정도 낮게 나타났다.

Recently, universities of fisheries and institutions related to fisheries are actively carrying out a project to build new fisheries training ships. These new fisheries training ships are significantly larger in size and longer in length than the previous ships. In addition, these new ships basically have space that can accommodate more than 100 crew and passenger. On the other hand, they are excluded from IMO maneuverability evaluation since the size of these ships are still less than 100 m in length (LBP). These results have had an impact on the study of maneuverability of fishing vessels including the fisheries training ships. Against these backgrounds, the authors conducted a study to estimate the maneuvering characteristics of fisheries training ship Baek-Kyung according to depth in order to prepare a maneuvering characteristic index that enables the large fisheries training ships to navigate more safely using a modified empirical formula. It was confirmed that the maneuvering characteristics of Baek-Kyung changed significantly as the values of the hydrodynamic force coefficients changed as the water depth gradually decreased from around 1.5 (approx. 8 m in depth) of the ratio of the water depth to the ship draft. The results of this study will not only help navigators understand the maneuvering characteristics of Baek-Kyung, but also serve as an indicator when navigating in shallow water. In addition, the accumulation of these results will serve as a basis for future study on maneuverability of fishing vessel types.

In this study, we aimed to determine the seasonal distribution and biomass of fish in Samcheok marine ranching area (MRA) of Republic of Korea using the scientific echosounder. Fish trap and gillnets were used to identify fish species in the survey area, and dB-difference method was used to estimation the spatio-temporal distribution and density of fish. The results showed that the dominant species in Samcheok marine ranching area were Chelidonichthys spinosus, Sebastes inermis, Hexagrammos otakii and Tribolodon hakonensis. The spatio-temporal distribution of fish showed that fish had a relatively higher distribution at night than during the day. In addition, the density of fish by season was highest at night in July at 34.22 g/m 2 and lowest in April at 0.42 g/m 2 .

본 논문에서는 한국전통게임에 사용되는 접이식 종이구조물(이하 딱지)의 접이과정을 모델링하고 게임의 승리조건을 만족시키는 충돌조건을 유전알고리즘을 이용하여 산정하는 과정을 서술하였다. 딱지는 A4용지 2장으로 구성되는 것을 가정하였다. 접이과정은 강제경계조건을 부여하여 날개부분을 꼬임의 위치로 변형시키고 강체판의 강제경계조건을 이용하여 딱지를 압착하였다. 이후 복원 력에 의한 완화해석을 수행하여 게임에 사용된 딱지의 형상과 응력상태를 구성하였다. 얻어진 동일한 2개의 딱지 중 타격딱지를 주어 진 충돌위치로 강제변위에 의해서 이동시키고 주어진 충돌속력에 대한 충돌해석으로 게임의 진행과정을 해석하였다. 이 때 승리조건 인 피격딱지의 반전을 일으키는 충돌조건을 산정하기 위하여 유전알고리즘을 이용한 최적화해석을 수행하였다. 이 과정에서 효율적 인 해석을 위하여 충돌해석을 2단계로 나누고 1단계의 해석결과 피격딱지에 반전이 발생할 가능성이 있는 경우에만 2단계해석을 진 행하였다. 1단계 해석에서 유전알고리즘의 적합함수는 피격딱지의 방향코사인이었고 2단계해석에서는 속도의 역수로 하여 전체적 으로는 가장 낮은 충돌속력을 가지는 충돌조건을 찾아내고자 하였다. 해석수행결과 다양한 압착두께에 따른 최적의 충돌조건을 찾아 낼 수 있었다.

본 연구는 경남지역 대나무림에 대한 면적 및 입목축적량 산정과 함께 탄소저장량을 추정하기 위하여 수행되었다. 현재 산림청의 임업통계연보에는 대나무림에 대한 공간정보인 면적만 제시되고 있을 뿐, 입목축적에 대한 정보는 전혀 제공되고 있지 못하다. 따라서 본 연구에서는 최근에 만들어진 대나무 재적표를 활용하여 경남지역의 입목축적량을 계산하고, 여기에 탄소배출계수를 이용하여 탄소저장 량을 계산하였다. 대나무림에 대한 면적은 1/5,000 수치임상도에서 추출하였으며, 입목축적을 산정하기 위해 현지조사한 표준지 자료를 활용하였다. 표준지의 대나무는 개체목 별로 흉고직경과 수고에 재적표를 적용하여 표준지의 재적을 산정하였다. 경남지역 대나무의 공간분포 면적은 총 6,038ha로서, 전국 대나무림(20,262 ha)중 약 30%를 차지하는 것으로 나타났다. 경남지역 중에서는 하동군이 가장 많은 면적인 1,027 ha가 분포하는 것으로 나타났다. 경남지역의 시계열적인 분포면적 변화는 2013년 대비 2022년에 약 20.3% 감소하였으 며, 2019년 대비 약 0.4%가 감소하였다. 경남지역 대나무의 입목축적량은 2022년말 현재 148,510 ㎥인 것으로 계산되었으며, ha당 축적은 약 25 ㎥으로 나타났다. 그리고 경남지역 대나무 숲의 이산화탄소저장량은 287,277 tCO2 이었으며, ha당 저장량은 47.6 tCO2 인 것으로 나타났다.