• Geomechanics and Engineering
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• 제17권5호
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• pp.485-496
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• 2019

Sealing of leakage in waterfront or water-retaining structures is one of the major issues in geotechnical engineering practices. With demands for biological methods as sustainable ground improvement techniques, bioclogging, defined as the reduction in hydraulic conductivity of soils caused by microbial activities, has been considered as an alternative to the chemical grout techniques for its economic advantages and eco-friendliness of microbial by-products. This study investigated the feasibility of bioaugmentation and biostimulation methods to induce fermentation-based bioclogging effect in coarse sands. In the bioaugmentation experiments, effects of various parameters and conditions, including grain size, pH, and biogenic gas generation, on hydraulic conductivity reduction were examined through a series of column experiments while Leuconostoc mesenteroides, which produce an insoluble biopolymer called dextran, was used as the model bacteria. The column test results demonstrate that the accumulation of bacterial biopolymer can readily reduce the hydraulic conductivity by three-to-four orders of magnitudes or by 99.9-99.99% in well-controlled environments. In the biostimulation experiments, two inoculums of indigenous soil bacteria sampled from waterfront embankments were prepared and their bioclogging efficiency was examined. With one inoculum containing species capable of fermentation and biopolymer production, the hydraulic conductivity reduction by two orders of magnitude was achieved, however, no clogging was found with the other inoculum. This implies that presence of indigenous species capable of biopolymer production and their population, if any, play a key role in causing bioclogging, because of competition with other indigenous bacteria. The presented results provide fundamental insights into the bacterial biopolymer formation mechanism, its effect on soil permeability, and potential of engineering bacterial clogging in subsurface.

• Geomechanics and Engineering
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• 제17권5호
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• pp.465-473
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• 2019

Biocementation due to the microbially induced calcium carbonate precipitation (MICP) process is a potential technique that can be used for soil improvement. However, the effect of biocementation may be affected by many factors, including nutrient concentration, bacterial strains, injection strategy, temperature, pH, and soil type. This study investigates mainly the effect of chemical concentration on the formation of calcium carbonate (e.g., quantity, size, and crystalline structure) and unconfined compressive strength (UCS) using different treatment time and chemical concentration in the biotreatment. Two chemical concentrations (0.5 and 1.0 M) and three different treatment times (2, 4, and 8 cycles) were studied. The effect of chemical concentrations on the treatment was also examined by making the total amount of chemicals injected to be the same, but using different times of treatment and chemical concentrations (8 cycles for 0.50 M and 4 cycles for 1.00 M). The UCS and CCC were measured and scanning electron microscopy (SEM) analysis was carried out. The SEM images revealed that the sizes of calcium carbonate crystals increased with an increase in chemical concentrations. The UCS values resulting from the treatments using low concentration were slightly greater than those from the treatments using high concentration, given the CCC to be more or less the same. This trend can be attributed to the size of the precipitated crystals, in which the cementation efficiency increases as the crystal size decreases, for a given CCC. Furthermore, in the high concentration treatment, two mineral types of calcium carbonate were precipitated, namely, calcite and amorphous calcium carbonate (ACC). As the crystal shape and morphology of ACC differ from those of calcite, the bonding provided by ACC can be weaker than that provided by calcite. As a result, the conditions of calcium carbonate were affected by test key factors and eventually, contributed to the UCS values.

• 한국추진공학회지
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• 제24권3호
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• pp.1-11
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• 2020

본 연구에서는 lab-scale 젤로켓모터를 이용하여 액상 케로신 연료와 이를 젤화 시킨 젤 케로신 연료의 연소시험을 수행함으로써, 각 연료별 연소실 정압특성 및 동압특성에 대해 분석하였다. 액상 케로신과 젤 케로신의 정압, 특성속도 및 특성속도 효율은 예상외로 큰 차이를 보이지 않음을 확인하였다. 그러나 액상 케로신과 젤 케로신의 동압 특성을 비교한 결과, 특정 고주파수 영역에서 젤 케로신의 압력 진폭이 액상 케로신 대비 증가하는 것을 확인하였다. 이는 젤 추진제의 고유 연소 메커니즘에 기인한 특성으로 여겨지며, 이들 압력 섭동 진폭이 추후 젤로켓모터의 고주파 연소불안정에 영향을 미치는 주요 인자로 적용될 가능성이 클 것으로 판단되었다.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2009년도 추계학술대회
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• pp.175-178
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• 2009

H.264/AVC는 ITU-T와 ISO/IEC의 최신 동영상 압축 코덱 규격으로 MPEG-2보다 2배 이상의 압축률과 고화질로 최근 그 적용 영역을 넓혀 가고 있다. 본 논문에서는 H.264/AVC에서 압축 성능을 높이기 위해 사용된 기법중 하나인 인트라 예측에 대해 설명하고 인트라 예측 모드 연산을 효율적으로 수행하기 위한 인트라 예측기의 구조를 제안하다. 제안된 인트라 예측기는 공통 연산기와 전처리 연산기를 사용하여 연산량을 줄이고, 효율적인 레지스터를 사용하여 외부 메모리와의 접근을 최소화 하였다. 제안된 인트라 예측기는 Verilog-HDL을 이용하여 설계하였으며 적합한 테스트 벡터를 이용하여 검증 되었다. 제안된 인트라 예측기는 기존에 비해 약 60%의 향상된 결과를 얻었다.

• 융합정보논문지
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• 제9권10호
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• pp.87-93
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• 2019

치유 물질인 아마인유에 형광 물질을 소량 혼합한 혼합물을 코어 성분으로 하여 마이크로캡슐을 제조하였다. 이 마이크로캡슐을 코팅재 조성물에 균일하게 분산시켜 모르타르 표면에 도포함으로써 지기치유 보호코팅재를 제작하였다. 이 코팅재에 약 $100{\mu}m$ 폭의 스크래치 또는 균열 손상을 발생시킨 후 자외선을 조사하면서 관찰한 결과, 캡슐 함량이 증가할수록 형광 발광의 범위가 증가하였고, 20wt% 이상의 캡슐을 함유한 코팅재에서는 손상 부위가 치유물질로 거의 채워진 모습을 나타내었다. 물흡수도 시험 결과 20wt% 이상의 캡슐을 함유한 코팅재는 약 85%의 치유효율을 보였다. 손상 부위에서 나타나는 형광은 3m의 거리에서도 용이하게 식별되었다. 본 연구결과는 콘크리트 보호를 위한 마이크로캡슐형 자기치유 코팅재의 치유기능을 육안으로 용이하게 확인하는데 도움을 줄 것으로 기대된다.

• 한국물환경학회지
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• 제35권5호
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• pp.432-438
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• 2019

The purpose of this study is to predict Trophic Diatom Index (TDI) in tributaries of the Han River watershed using the random forest algorithm. The one year (2017) and supplied aquatic ecology health data were used. The data includes water quality(BOD, T-N, $NH_3-N$, T-P, $PO_4-P$, water temperature, DO, pH, conductivity, turbidity), hydraulic factors(water width, average water depth, average velocity of water), and TDI score. Seven factors including water temperature, BOD, T-N, $NH_3-N$, T-P, $PO_4-P$, and average water depth are selected by the Correlation Feature Selection. A TDI prediction model was generated by random forest using the seven factors. To evaluate this model, 2017 data set was used first. As a result of the evaluation, $R^2$, % Difference, NSE(Nash-Sutcliffe Efficiency), RMSE(Root Mean Square Error) and accuracy rate show that this model is compatible with predicting TDI. To be more concrete, $R^2$ is 0.93, % Difference is -0.37, NSE is 0.89, RMSE is 8.22 and accuracy rate is 70.4%. Also, additional evaluation using data set more than 17 times the measured point was performed. The results were similar when the 2017 data set were used. The Wilcoxon Signed Ranks Test shows there was no statistically significant difference between actual and predicted data for the 2017 data set. These results can specify the elements which probably affect aquatic ecology health. Also, these will provide direction relative to water quality management for a watershed that must be continuously preserved.

• 한국물환경학회지
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• 제36권6호
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• pp.581-591
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• 2020

In this study, two types of reactors were operated to examine the properties of methanol uptake under the high-rate denitrification process. In a sequencing batch reactor, the denitrifying activity was enriched up to 0.80 g-N/g-VSS-day for 72 days. Then, the enriched denitrifying sludge was transferred to a completely stirred tank reactor (CSTR). At the final phase on Day 46-50, the nitrogen removal efficiency was around 100% and the total nitrogen removal rate reached 0.097±0.003 kg-N/㎥-day. During the continuous process, the sludge settling index (SVI30) was stabilized as 118.3 mL/g with the biomass concentration of 1,607 mg/L. The continuous denitrifying process was accelerated by using a sequencing batch reactor (SBR) with a total nitrogen removal rate of 0.403±0.029 kg-N/㎥-day with a high biomass concentration of 8,433 mg-VSS/L. Because the reactor was open to ambient air with the dissolved oxygen range of 0.2-0.5 mg-O2/L, an increased organic carbon requirement of 5.58±0.70 COD/NO3--N was shown for the SBR in comparison to the value of 4.13±0.94 for the test of the same biomass in a completely anaerobic batch reactor. The molecular analysis based on the 16S rRNA gene showed that Methyloversatilis discipulorum and Hyphomicrobium zavarzinii were the responsible denitrifiers with the sole organic carbon source of methanol.

• 융합정보논문지
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• 제11권2호
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• pp.90-97
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• 2021

본 연구에서는 산업현장에서 미활용되는 열에너지를 회수하여 유용한 전기에너지로 변환하기 위한 Bi2Te3 계열 열전소자를 제작하고 에너지회수 성능 및 물성을 도출하였다. 성능시험을 위하여 카트리지 히터 가열 방식의 가열블록과 냉매가 흐르는 냉각블록으로 구성된 전용 실험장치를 구성하였으며, 가열블록과 냉각블록에는 3×3 배열의 열전대를 장착하여 소자 양 면 온도와 열전달율을 도출하였다. 최소 온도차 27K부터 최대 온도차 172.2K까지 총 9가지의 온도차에 대해 실험을 수행하여 V-I curve와 P-R curve를 도출하였고 성능에 주요한 영향을 미치는 제벡계수 등 변수 7가지에 대하여 온도차에 대한 함수로 결과를 제시하였다. 최대 발전양 7.5W와 변환효율 11.3%의 결과로부터 개발된 열전소자의 열에너지 회수 성능의 타당성을 검증하였다.

• 마이크로전자및패키징학회지
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• 제28권2호
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• pp.45-50
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• 2021

Flip chip 제품의 난이도 증가에 따라 solder wetting 및 신뢰성 관점에서 강점을 갖는 flux 소재에 대한 관심이 높아지고 있다. 지용성 flux의 경우 별도의 세정 공정이 없기 때문에 공정 효율화 측면에서 유리하나, 리플로우 공정이후 반응을 마친 잔여물이 잔존하게 되는 경우 Cu migration 및 delamination을 발생시킬 수 있다. 본 연구에서는 저잔사 flux 구현을 위해 신규 resin에 적합한 solvent 및 activator를 변경 하였으며, package 환경에서 non-wet 및 신뢰성 개선 유무를 확인하였다. 저장 안정성 평가를 통해 신규 소재에 대한 안정성을 확보하였으며, boiling point가 상이한 solvent와 activator 2종 적용 및 activator 함량 증대를 통해 non-wet 미 발생 flux 소재를 확보하였다. 해당 소재에 대한 신뢰성 검증 이후 평면 분석 결과 flux residue 기인성 delamination 현상은 발견되지 않았으며, 이를 통해 저잔사 flux에 대한 최종 조성을 확보하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권6호
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• pp.1957-1980
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• 2021

The significant aim of this research has always been to evaluate the mechanism for efficient and inherently aware usage of vitality in-home devices, thus improving the information of smart metering systems with regard to the usage of selected homes and the time of use. Advances in information processing are commonly used to quantify gigantic building activity data steps to boost the activity efficiency of the building energy systems. Here, some smart data mining models are offered to measure, and predict the time series for energy in order to expose different ephemeral principles for using energy. Such considerations illustrate the use of machines in relation to time, such as day hour, time of day, week, month and year relationships within a family unit, which are key components in gathering and separating the effect of consumers behaviors in the use of energy and their pattern of energy prediction. It is necessary to determine the multiple relations through the usage of different appliances from simultaneous information flows. In comparison, specific relations among interval-based instances where multiple appliances use continue for certain duration are difficult to determine. In order to resolve these difficulties, an unsupervised energy time-series data clustering and a frequent pattern mining study as well as a deep learning technique for estimating energy use were presented. A broad test using true data sets that are rich in smart meter data were conducted. The exact results of the appliance designs that were recognized by the proposed model were filled out by Deep Convolutional Neural Networks (CNN) and Recurrent Neural Networks (LSTM and GRU) at each stage, with consolidated accuracy of 94.79%, 97.99%, 99.61%, for 25%, 50%, and 75%, respectively.