• Journal of Microbiology and Biotechnology
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• 제32권9호
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• pp.1195-1208
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• 2022

Silver nanoparticles (AgNPs) have potential applications in medicine, photocatalysis, agriculture, and cosmetic fields due to their unique physicochemical properties and strong antimicrobial activity. Here, AgNPs were synthesized using actinobacterial SL19 strain, isolated from acidic forest soil in Poland, and confirmed by UV-vis and FTIR spectroscopy, TEM, and zeta potential analysis. The AgNPs were polydispersed, stable, spherical, and small, with an average size of 23 nm. The FTIR study revealed the presence of bonds characteristic of proteins that cover nanoparticles. These proteins were then studied by using liquid chromatography with tandem mass spectrometry (LC-MS/MS) and identified with the highest similarity to hypothetical protein and porin with molecular masses equal to 41 and 38 kDa, respectively. Our AgNPs exhibited remarkable antibacterial activity against Escherichia coli and Pseudomonas aeruginosa. The combined, synergistic action of these synthesized AgNPs with commercial antibiotics (ampicillin, kanamycin, streptomycin, and tetracycline) enabled dose reductions in both components and increased their antimicrobial efficacy, especially in the case of streptomycin and tetracycline. Furthermore, the in vitro activity of the AgNPs on human cancer cell lines (MCF-7, A375, A549, and HepG2) showed cancer-specific sensitivity, while the genotoxic activity was evaluated by Ames assay, which revealed a lack of mutagenicity on the part of nanoparticles in Salmonella Typhimurium TA98 strain. We also studied the impact of the AgNPs on the catalytic and photocatalytic degradation of methyl orange (MO). The decomposition of MO was observed by a decrease in intensity of absorbance within time. The results of our study proved the easy, fast, and efficient synthesis of AgNPs using acidophilic actinomycete SL19 strain and demonstrated the remarkable potential of these AgNPs as anticancer and antibacterial agents. However, the properties and activity of such particles can vary by biosynthesized batch.

• 방송공학회논문지
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• 제27권6호
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• pp.872-884
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• 2022

IoT 산업 발전으로 다양한 산업군에서 서로 다른 형태의 시계열 데이터를 생성하고 있으며 이를 다시 통합하여 재생산 및 활용하는 연구로 진화하고 있다. 더불어, 실제 산업에서 데이터 처리 속도 및 활용 시스템의 이슈 등으로 인해 시계열 데이터 활용 시 데이터의 크기를 압축하여 통합 활용하는 경향이 증가하고 있다. 그러나 시계열 데이터의 통합 가이드라인이 명확하지 않고 데이터 기술 시간 간격, 시간 구간 등 각각의 특성이 달라 일괄 통합하여 활용하기 어렵다. 본 논문에서는 통합 기준 설정 방법과 시계열 데이터의 통합시 발생하는 문제점을 기반으로 두 가지의 통합 방법을 제시하였다. 이를 기반으로 시계열 데이터의 특성을 고려한 이질적 시계열 데이터 통합 프레임워크를 구성하였으며 압축된 서로 다른 이질적 시계열 데이터의 통합과 다양한 기계 학습에 활용할 수 있음을 확인하였다.

• 한국지하수토양환경학회지:지하수토양환경
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• 제27권1호
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• pp.39-49
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• 2022

Bottom ash generated from thermal power plants is mainly disposed in landfills, from which metals may be leached by infiltrating water. To evaluate the effect of metals in leachate on soil and groundwater, we characterized bottom ash generated from burning cokes, bituminous coal, the mixture of bituminous coal and wood pellets, and charcoal powder. The bottom ash of charcoal powder had a relatively large particle size, and its wood texture was well-preserved from SEM observation. The bottom ash of charcoal powder and wood pellets had relatively high K concentration from total element analysis. The eluates of the bottom ash samples had appreciable concentrations of Ca, Al, Fe, SO₄, and NO₃, but they were not a significant throughout the batch test. Therefore, it is considered that there is low possibility of soil and groundwater contamination due to leaching of metal ions and anions from these bottom ash in landfills. To estimate the trend of various trace elements, long-term monitoring and additional analysis need to be performed while considering the site conditions, because they readily adsorb on soil and aquifer substances.

• 화약ㆍ발파
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• 제40권3호
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• pp.33-43
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• 2022

화약류 저장소는 내부 폭발 시 구조물에 가해지는 영향을 최소화하기 위해 내벽과 일정 거리 이격하여 화약류를 저장하고, 예기치 못한 폭발에 나머지 폭약의 순폭을 방지해야 한다. 따라서 안전한 화약류 저장을 위해 저장소 내부에 폭약의 분할 배치를 모사하여 순폭 실험을 진행하였다. 본 연구에서는 에멀젼 폭약 사이의 이격거리, 배치를 달리 적용하여 직경의 2배(2D)에서 순폭되고, 2.5배(2.5D)에서 불폭됨을 확인하였다. 순폭도와 화약류 저장소 크기를 감안하여 폭약량 3kg을 설정하였고 다양한 배치 변화에 따른 순폭실험 결과 대부분 불폭되어 해당 배치 적용 시 안전성을 확인하였다.

• 산업경영시스템학회지
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• 제45권4호
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• pp.233-239
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• 2022

The injection molding process is a process in which thermoplastic resin is heated and made into a fluid state, injected under pressure into the cavity of a mold, and then cooled in the mold to produce a product identical to the shape of the cavity of the mold. It is a process that enables mass production and complex shapes, and various factors such as resin temperature, mold temperature, injection speed, and pressure affect product quality. In the data collected at the manufacturing site, there is a lot of data related to good products, but there is little data related to defective products, resulting in serious data imbalance. In order to efficiently solve this data imbalance, undersampling, oversampling, and composite sampling are usally applied. In this study, oversampling techniques such as random oversampling (ROS), minority class oversampling (SMOTE), ADASYN(Adaptive Synthetic Sampling), etc., which amplify data of the minority class by the majority class, and complex sampling using both undersampling and oversampling, are applied. For composite sampling, SMOTE+ENN and SMOTE+Tomek were used. Artificial neural network techniques is used to predict product quality. Especially, MLP and RNN are applied as artificial neural network techniques, and optimization of various parameters for MLP and RNN is required. In this study, we proposed an SA technique that optimizes the choice of the sampling method, the ratio of minority classes for sampling method, the batch size and the number of hidden layer units for parameters of MLP and RNN. The existing sampling methods and the proposed SA method were compared using accuracy, precision, recall, and F1 Score to prove the superiority of the proposed method.

• 한국인터넷방송통신학회논문지
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• 제22권6호
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• pp.113-119
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• 2022

소프트웨어 결함 예측 작업 시 단순히 결함 유무만을 예측하는 이진 분류 모델에 비해 결함의 심각도 범주를 예측하는 다중 분류 모델은 훨씬 유용하게 사용될 수 있다. 소수의 심각도 기반 결함 예측 모델들이 제안되었지만 딥러닝 기법을 사용한 분류기는 없었다. 본 논문은 3개, 5개의 은닉층을 갖고 은닉층 노드수가 고정된 구조와 변화하는 구조의 MLP 모델들을 제작하였다. 모델 평가 실험 결과 기존 기계학습 모델들 중 가장 좋은 성능을 보인 MLPs보다 MLP 기반 딥러닝 모델들은 Accuracy와 AUC 모두 유의미하게 더 우수한 성능을 보였다. 특히 노드수 고정 구조에서는 은닉 층수 3, 배치사이즈 32, 노드수 64인 모델 구조가 가장 좋은 성능을 보였다.

• 아태비즈니스연구
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• 제14권2호
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• pp.101-118
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• 2023

Purpose - The purpose of this study is to find out how a company can do reasonable cost calculations in a simple way and establish profitability improvement strategies based on the results. Design/methodology/approach - In this study, a case that compounding company K applied TDABC was studied. A case study was conducted on the process of company K reviewing and applying TDABC and the process of implementing the cost calculation for each product by applying TDABC, and establishing a profitability improvement strategy for each product based on the results. Findings - Company K rearranged the production standard information of the compounding industry such as productivity and batch size of each product to apply TDABC. Cost calculation was performed for each product according to the revised production standard information. After the cost calculation for each product was carried out, Company K established a strategy to improve profitability of each product. The profitability improvement strategy was implemented in two ways: a cost reduction strategy and a product price increase strategy. As a result of the final strategy execution, the profitability of each product was improved. Research implications or Originality - This study found a reasonable costing standard in consideration of the specificity of the research target company, and applied it to cost calculation cost for each product. It contains the process of establishing production and sales strategies for each product based on the cost calculation results. It is expected that this case study will serve as a good reference material for establishing cost calculation and profitability improvement strategies in similar businesses.

• Geomechanics and Engineering
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• 제37권1호
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• pp.49-64
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• 2024

Rocks undergoing repeated loading and unloading over an extended period, such as due to earthquakes, human excavation, and blasting, may result in the gradual accumulation of stress and deformation within the rock mass, eventually reaching an unstable state. In this study, a CNN-CCM is proposed to address the mechanical behavior. The structure and hyperparameters of CNN-CCM include Conv2D layers × 5; Max pooling2D layers × 4; Dense layers × 4; learning rate=0.001; Epoch=50; Batch size=64; Dropout=0.5. Training and validation data for deep learning include 71 rock samples and 122,152 data points. The AI Rock Constitutive Model learned by CNN-CCM can predict strain values(ε₁) using Mass (M), Axial stress (σ₁), Density (ρ), Cyclic number (N), Confining pressure (σ₃), and Young's modulus (E). Five evaluation indicators R², MAPE, RMSE, MSE, and MAE yield respective values of 0.929, 16.44%, 0.954, 0.913, and 0.542, illustrating good predictive performance and generalization ability of model. Finally, interpreting the AI Rock Constitutive Model using the SHAP explaining method reveals that feature importance follows the order N > M > σ₁ > E > ρ > σ₃.Positive SHAP values indicate positive effects on predicting strain ε₁ for N, M, σ₁, and σ₃, while negative SHAP values have negative effects. For E, a positive value has a negative effect on predicting strain ε₁, consistent with the influence patterns of conventional physical rock constitutive equations. The present study offers a novel approach to the investigation of the mechanical constitutive model of rocks under cyclic loading and unloading conditions.

• 한국재료학회지
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• 제26권12호
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• pp.733-740
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• 2016

$Al_2O_3$ nanosol dispersed under ethanol or N-Methyl-2-pyrrolidone(NMP) was studied and optimized with various dispersion factors and by utilizing the silane modification method. The two kinds of $Al_2O_3$ powders used were prepared by thermal decomposition method from aluminum ammonium sulfate$(AlNH_4(SO_4)_2)$ while controlling the calcination temperature. $Al_2O_3$ sol was prepared under ethanol solvent by using a batch-type bead mill. The dispersion properties of the $Al_2O_3$ sol have a close relationship to the dispersion factors such as the pH, the amount of acid additive(nitric acid, acetic acid), the milling time, and the size and combination of zirconia beads. Especially, $Al_2O_3$ sol added 4 wt% acetic acid was found to maintain the dispersion stability while its solid concentration increased to 15 wt%, this stability maintenance was the result of the electrostatic and steric repulsion of acetic acid molecules adsorbed on the surface of the $Al_2O_3$ particles. In order to observe the dispersion property of $Al_2O_3$ sol under NMP solvent, $Al_2O_3$ sol dispersed under ethanol solvent was modified and solvent-exchanged with N-Phenyl-(3-aminopropyl)trimethoxy silane(APTMS) through a binary solvent system. Characterization of the $Al_2O_3$ powder and the nanosol was observed by XRD, SEM, ICP, FT-IR, TGA, Particles size analysis, etc.

• 한국지하수토양환경학회지:지하수토양환경
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• 제10권1호
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• pp.58-64
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• 2005

본 연구는 플럭 형성 비소 오염토양에 대한 토양세척기법 적용시 최적의 운전조건을 도출하고자 하였으며, 대상 시료는 D 폐철광산 토양을 선정하였다. 최적의 cut-off size는 전체 토양 중량에 대하여 약 $94\%$ 정도의 분포를 보이는 0.15 mm ($\#100$체)이었다. 수산화나트륨과 염산 모두 비소 제거에 효과적이었고, 진탕비 (토양[g]:세척용액[mL])는 2가지 세척제에 대하여 1:5가 최적 조건임을 알 수 있었다. 토양세척시 형성되는 플럭에 대하여 비소 농도를 파악한 결과, 여타 pH조건에서보다 pH $5\~6$에서 형성된 플럭의 건조 비소 농도가 $990\~1,086\;mg/kg$ dry solids로 높음을 알 수 있었다. 따라서, 세척효율의 향상 여부를 파악하기 위하여 토양세척시 형성되는 플럭의 제거 유무에 따른 연속 토양세척 실험을 수행하였다. 0.2 M염산을 사용하여 플럭을 제거한 토양을 염산 1 M로 세척한 다음 1 M 수산화나트륨으로 연속 세척한 결과, 비소 농도는 약 1.5 mg/kg dry soil을 보였다. 각 단계마다 발생된 세척유출수기 비소 농도는 약 $2\~3\;mg/L$이었으나, 각각의 세척유출수를 혼합하는 경우 비소 농도가 $50\;{\mu}g/L$ 이하로 감소되었는제 이는 비소가 응집${\cdot}$침전으로 제거되는데 유리한 pH조건으로 변환되기 때문인 것으로 판단된다.