본 연구는 데이터를 기반으로 한 인공지능 기계학습 기법을 활용하여 온실 내부온도 예측 시뮬레이션 모델을 개발을 수행 하였다. 온실 시스템의 내부온도 예측을 위해서 다양한 방법 이 연구됐지만, 가외 변인으로 인하여 기존 시뮬레이션 분석 방법은 낮은 정밀도의 문제점을 지니고 있다. 이러한 한계점 을 극복하기 위하여 최근 개발되고 있는 데이터 기반의 기계 학습을 활용하여 온실 내부온도 예측 모델 개발을 수행하였 다. 기계학습모델은 데이터 수집, 특성 분석, 학습을 통하여 개 발되며 매개변수와 학습방법에 따라 모델의 정확도가 크게 변 화된다. 따라서 데이터 특성에 따른 최적의 모델 도출방법이 필요하다. 모델 개발 결과 숨은층 증가에 따라 모델 정확도가 상승하였으며 최종적으로 GRU 알고리즘과 숨은층 6에서 r2 0.9848과 RMSE 0.5857℃로 최적 모델이 도출되었다. 본 연 구를 통하여 온실 외부 데이터를 활용하여 온실 내부온도 예 측 모델 개발이 가능함을 검증하였으며, 추후 다양한 온실데이 터에 적용 및 비교분석이 수행되어야 한다. 이후 한 단계 더 나아 가 기계학습모델 예측(predicted) 결과를 예보(forecasting)단 계로 개선하기 위해서 데이터 시간 길이(sequence length)에 따른 특성 분석 및 계절별 기후변화와 작물에 따른 사례별로 개발 모델을 관리하는 등의 다양한 추가 연구가 수행되어야 한다.

지구온난화 완화를 위한 노력이 전 세계적으로 계속되고 있는 가운데 우리나라는 2030년 온실가스 배출량을 Business As Usual (BAU) 대비 37% 감축하는 목표를 설정하고 이산화탄소 순 배출 제로 달성을 목표로 하는 ‘2050탄소중립 시나리오’를 발표하였다. 이러한 상황에서 케나프 (Kenaf)는 높은 탄소흡수율과 빠른 성장으로 인해 대체 해결방안으로 제안된다. 시료선정에 대한 분석결과, 케나프 잎은 질소 함량이 높아 적합하지 않은 것으로 나타났고, 줄기와 잎-줄기 혼합 케나프는 Biomass-Solid Refuse Fuel (Bio-SRF) 등급을 충족했다. Bio-SRF 등급을 만족하였지만, Bio-SRF 등급으로 이용할 수 있는 활용처가 뚜렷하지 않다. 따라서 케나프의 연료품질을 반탄화 공정을 통해 개선하여 보다 효율적으로 활용하고자 한다. 반탄화 공정의 공시재료로 줄기만 사용하는 것보다 시료 이용률이 높은 잎-줄기 혼합 케나프를 선택하였다. 반탄화 공정은 여섯가지의 공정 온도 (200·210·220·230·240·250℃)와 다른 세 가지 공정 시간(20·30·40 min)에서 수행되었다. 반탄화 공정결과, 공정시간이 길수록 230℃, 240℃, 250℃ 온도에서 질량감소가 크기 때문에 에너지수율이 낮았다. 따라서 본 연구에서는 200℃·20 min, 200℃·30 min, 210℃·20 min, 220℃·20 min의 에너지 수율이 높고 질량감소가 작아 최적 반탄화 공정 조건으로 선정하였다.

The occurrence of cyanobacterial blooming and the contaminant sources were analyzed in the downstream of Jecheon Stream, a tributary of Chungju Reservoir. The concentrations of chlorophyll a at the Myungseo Fishing Point (GPS; 37°03′25.5″N, 128°03′13.6″E) were 399.2 and 184.8 mg m-3 on October 18, 2015 and September 25, 2016, respectively, and the concentrations of total microcystins, a cyanobacterial toxin mainly produced by Microcystis, were 124.09 and 79.71 μg L-1, respectively. The occurrence of cyanobacterial blooming at the downstream of Jecheon Stream was closely related to the water level of Chungju Reservoir. The cyanobacterial blooming occurred after the increase of water level in Chungju Reservoir, when the water body stagnated. As a result of analyzing National Water Quality Monitoring Data of the upper region of Jecheon Stream, the main source of pollutant was Jangpyeong Stream, the tributary of Jecheon Stream, and the discharge water from Jecheon Wastewater Treatment Plant located in Jangpyeong Stream was considered to be the most important source of contaminant.

PURPOSES : The objective of this study is to present a practical application method for measuring the setting time of concrete in the field, considering different concrete blending and curing environments. METHODS : In the experimental environment of the concrete setting experiment, a mold was made in the laboratory, and the curing temperature was performed at laboratory room temperature (20-25 ℃), heater (30 ℃), and dryer (40 ℃). In the field, the experimental method was performed in summer and autumn, setting experiments on penetration resistance, ultrasonic pulse velocity, and semi-calorimeter. RESULTS : The result of the concrete setting time experiment was that the early setting time was achieved in the combination of filash and slag when the concrete was mixed, and the setting time in the curing environment was the same as the early setting time in the field. The setting time measurement method shows the correlation of the high crystal coefficient at UPV with 0.99 based on PR and the good ratio of 92 % to 107 % on average. In addition, the semi-calorimeter shows a good crystal coefficient of 0.96 to 0.99 and a good setting time of 88 % to 101 % on average. CONCLUSIONS : The analysis of the setting time of the concrete shows that the curing temperature is more affected than the mixture, and it is considered that the UPV test, which evaluates the reliability of the degree of purification, is easy to sampie among the three methods.

본 연구는 설악산국립공원 아고산대에서 출현하는 주요 식생의 지역별, 해발고별 식생구조를 파악하고, 식물상 목록을 작성하고자 수행되었다. 기존 아고산대 연구결과를 고찰하고, 사전조사를 통해 아고산대 식생이 처음 출현하는 지점을 남측을 기준으로 800m로 결정하고 그곳에서부터 해발고도 50m 간격으로 북측 백담사~마등령 탐방로(BD) 12개소, 서측 한계령~끝청 탐방로(HG) 13개소, 동측 신흥사~소청봉 탐방로(SA) 13개소, 남측 오색~끝청, 오색~대청봉 탐방로(OS) 39개소에 각각 조사구를 설치하여 총 77개소에서 현장조사를 수행하였다. 분석결과, 아고산대 주요 식물인 신갈나무, 사스래나무, 분비나무 등의 출현 여부에 따른 종조성과 지역 및 해발고도에 의해 신갈나무-전나무-당단풍나 무군락, 신갈나무-피나무군락, 신갈나무-잣나무군락, 신갈나무-당단풍나무군락, 사스래나무-분비나무군락, 잣나무-분 비나무군락, 낙엽활엽혼효군락의 총 7개 군락으로 분류되었다. 조사구(100㎡)당 10.68±2.98종이 출현하였고, 110.87± 63.89개체가 확인되었다. 종다양성지수 분석결과, 설악산국립공원 아고산대 식생군락은 다양한 수종이 중요종으로 출현하는 혼효림으로 판단되었다. 지역별로 아고산대 주요 식물이 출현하는 최초 해발고도는 차이가 있었지만, 1,100~ 1,300m 구간에서 집중적으로 분포하고 있었다. 관속식물은 총 83과 193속 289종 4아종 28변종 1품종 322분류군으로 파악되었다. 법정보호종은 기생꽃 1분류군이 확인되었다. 한반도 고유종은 19종이 확인되었으며, 아고산식물은 58분류 군이 확인되었다.

PURPOSES : Road surface conditions are vital to traffic safety, management, and operation. To ensure traffic operation and safety during periods of snow and ice during the winter, each local government allocates considerable resources for monitoring that rely on field-oriented manual work. Therefore, a smart monitoring and management system for autonomous snow removal that can rapidly respond to unexpected abrupt heavy snow and black ice in winter must be developed. This study addresses a smart technology for automatically monitoring and detecting road surface conditions in an experimental environment using convolutional neural networks based on a CCTV camera and infrared (IR) sensor data. METHODS : The proposed approach comprises three steps: obtaining CCTV videos and IR sensor data, processing the dataset acquired to apply deep learning based on convolutional neural networks, and training the learning model and validating it. The first step involves a large dataset comprising 12,626 images extracted from the acquired CCTV videos and the synchronized surface temperature data from the IR sensor. In the second step, image frames are extracted from the videos, and only foreground target images are extracted during preprocessing. Hence, only the area (each image measuring 500 × 500) of the asphalt road surface corresponding to the road surface is applied to construct an ideal dataset. In addition, the IR thermometer sensor data stored in the logger are used to calculate the road surface temperatures corresponding to the image acquisition time. The images are classified into three categories, i.e., normal, snow, and black-ice, to construct a training dataset. Under normal conditions, the images include dry and wet road conditions. In the final step, the learning process is conducted using the acquired dataset for deep learning and verification. The dataset contains 10,100 (80%) data points for deep learning and 2,526 (20%) points for verification. RESULTS : To evaluate the proposed approach, the loss, accuracy, and confusion matrix of the addressed model are calculated. The model loss refers to the loss caused by the estimated error of the model, where 0.0479 and 0.0401 are indicated in the learning and verification stages, respectively. Meanwhile, the accuracies are 97.82% and 98.00%, respectively. Based on various tests that involve adjusting the learning parameters, an optimized model is derived by generalizing the characteristics of the input image, and errors such as overfitting are resolved. This experiment shows that this approach can be used for snow and black-ice detections on roads. CONCLUSIONS : The approach introduced herein is feasible in road environments, such as actual tunnel entrances. It does not necessitate expensive imported equipment, as general CCTV cameras can be applied to general roads, and low-cost IR temperature sensors can be used to provide efficiency and high accuracy in road sections such as national roads and highways. It is envisaged that the developed system will be applied to in situ conditions on roads.

Pancreatic acinar cell carcinoma (ACC) is a rare neoplasm accounting less than 1% of malignant pancreatic tumors. A 47-year-old male patient visited the emergency room with epigastric pain. Computed tomography or magnetic resonance imaging revealed a 4.7-cm heterogeneously enhanced solid and cystic mass with internal necrosis located in the head of the pancreas. Radiological diagnosis was borderline malignancy such as neuroendocrine tumor or solid pseudopapillary neoplasm. Two months later, the necrotic mass in the pancreas head had grown up to 11 cm, compressing the duodenum, superior mesenteric vein, and proximal transverse colon. Pylorus preserving pancreatoduodenectomy with segmental resection of transverse colon was performed. Histopathological examination revealed that the tumor was pancreatic ACC. The patient recovered without any complication and was doing well without recurrence for 12 months after surgery.

Breakthrough analysis has widely been explored for the dynamic separation of gaseous mixtures in porous materials. In general, breakthrough experiments measure the components of a flowing gas when a gaseous mixture is injected into a column filled with an adsorbent material. In this paper, we report on the design and fabrication of a breakthrough curve measurement device to study the dynamic adsorptive separation of hydrogen isotopologues in porous materials. Using the designed system, an experiment was conducted involving a 1:1 mixture of hydrogen and deuterium passed through a column filled with zeolite 13X (1 g). At room temperature, both hydrogen and deuterium were adsorbed in negligible amounts; however, at a temperature of 77 K, deuterium was preferentially adsorbed over hydrogen. The selectivity was different from that in the existing literature due to the different sample shapes, measurement methods, and column structures, but was at a similar level to that of cryogenic distillation (1.5).

정부는 공유수면 매립사업의 계획적인 관리를 위해, 10년 주기의 공유수면 매립기본계획을 수립하고 있다. 그러나 수시변경을 통한 매립사업을 추진하는 경우가 상당한 비중을 차지하고 있는 것으로 나타났다. 이에 기본계획의 실효성에 대한 의문이 제기되고 있으 며, 이를 보완하기 위한 장기 매립 수요 추세 분석에 대한 필요성이 증가하고 있다. 이에 본 연구에서는 그간의 연간 매립 실적 자료를 활용하여 매립 수요 추세 분석을 수행하였다. 분석 결과, 국내 공유수면 매립 수요는 지속적으로 하락하는 추세인 것으로 나타났으며, 특 히 매립기본계획 체제로 전환된 1990년대 이후에는 그 추세가 뚜렷하게 나타나고 있는 것으로 나타났다. 또한 2021-2030년까지 총 매립 수요는 최대 13.8 km2에서 최소 1.7 km2 수준으로 산정되었다.

본 연구는 염화나트륨에 의한 비생물적 스트레스가 생육과정 중의 느타리에게 미치는 영향을 자실체의 생육특성, 그리고 구성아미노산함량과 향기패턴 분석을 통해 확인하였다. 농도별로 염화나트륨을 처리한 톱밥배지에서 수확한 느타리 자실체의 생육특성을 조사한 결과, 처리구별 수량은 무처리구 대비 염화나트륨 0.5%처리구에서의 값은 비슷 했으며, 염화나트륨 처리 농도가 1.0%에서 2.0%으로 증가하면서 수량이 확연히 감소하였다. 자실체의 구성아미노산함량 분석결과, 무처리구 대비 모든 염화나트륨 처리구에서 glutamic acid과 proline을 제외한 aspartate, threonine, serine, glycine, alanine, methionine, valine, isoleucine, leucine, phenylalanine, tyrosine, lysine, histidine, arginine의 14종 구성아미노산의 함량이 낮음을 확인하였다. 자실체의 향기패턴 분석결과, 염화나트륨 처리구에서 버섯고유의 향을 나타내는 octane compound로 추정되는 물질의 intensity가 감소하였음을 chromatography상에서 확인하였다.

본 논문의 목적은 ‘숲 명상’과 이를 기반으로 창작된 시문학이 현대인들의 상흔을 치유하고 위로하며, 생명연대의 공감을 일깨워 주고, 비움과 충만의 치유의 미학으로 중요한 기능을 하는 것임을 살펴보는 데 있다. 숲은 인류가 오랫동안 누려왔던 터전이자 고향이며, 숲과 교류하는 것은 인간의 본성을 찾는 길이다. 그래서 숲과 교감하고 숲을 통해 ‘내 안의 풍경’을 성찰하는 것은 상처를 치유하고 위로를 받을 수 있으며, 또한 번다하고 탐욕스러운 마음을 잠시나마 내려놓고 공감과 비움의 의미를 깨달게 한다 할 수 있다. 이는 숲이 단순한 휴식 공간의 차원을 넘어 치유의 공간으로 변모하고 있음을 시사한다. 따라서 ‘숲 명상’은 바른 지견으로 사물을 보고 욕망과 채움, 집착과 분노, 복잡과 불안을 극복할 수 있는 중요한 ‘힐링’ 방법 중의 하나라 할 수 있다.

In order to understand basic data for improving the fishing system and fishing vessel structure in coastal improved stow net fishery, a questionnaire survey and on-site hearing were conducted from May 10 to June 11, 2019 to analyze opinions on the improvement of operation status and fishing vessel structure. The questionnaire survey consisted of ten questions on the operation status of coastal improved stow net fishery and six questions on the improvement of fishing vessel structure, and the results of each question were analyzed by the region, the captain’s age, the captain’s career and the age of fishing vessel. As a result of analyzing opinions on the operation status of the coastal improved stow net fishery, it was found that the average time required for casting net was 32.8 to 33.0 minutes and that the average time required for hauling net was 41.0 to 42.2 minutes which took 10 to 12 minutes more than for casting net. The most important work requiring improvement during fishing operation (the first priority) were ‘hauling net operation,’ ‘readjustment and storage of fishing gear,’ and ‘fish handling’ and the hardest factor in fishing management were in the order of ‘reduction of catch,’ ‘labor shortage’ and ‘rising labor costs.’ The most institutional improvement that is most needed in coastal improved stow net fishery was an ‘using fine mesh nets.’ Most of the respondent to the questions on the experience in hiring foreign crews was ‘either hiring or willing to hire foreign crews,’ and the average number of foreign crews employed was found to be 2.3 to 2.4 persons. The most important reason for hiring (or considering employment) foreign crews was ‘high labor costs.’ The degree of communication with foreign crews during fishing operation were ‘moderate’ or ‘difficult to direct work.’ The most important problem in hiring foreign crews (the first priority) was an ‘illegal departure.’ As the survey results on the opinion of structural improvement of coastal improved stow net fishing vessel, the degree of satisfaction with fishing vessel structure related to fishing operation was found to be somewhat unsatisfactory, with an average of 3.3 points on a five-point scale. The inconvenient structure of fishing vessel in possession (the first priority), the space needed most for the construction of new fishing vessel (the first priority) and the space considered important for the construction of new fishing vessel (the first prioprity) was a ‘fish warehouse.’ The most preferred equipment for the construction of new fishing vessel were ‘engine operation monitoring’ and ‘navigation safety devices.’ The average size (tonnage class), the average horse power and the average total length of fishing vessel for proper profit and safety fishing operation was between 13.8 and 14.0 tonnes, 808.3 to 819.5 H.P. and 23.4 to 23.5 meters, respectively. The results of the operation status of coastal improved stow net fishery and the requirement for improving the fishing vessel structure are expected to be provided as basic data for reference when we build or improve the fishing vessel.