• Title/Summary/Keyword: 실험기법

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Oxidative Desulfurization of Marine Diesel Using Keggin Type Heteropoly Acid Catalysts (Keggin형 헤테로폴리산 촉매를 이용한 선박용 경유의 산화 탈황)

  • Oh, Hyeonwoo;Woo, Hee Chul
    • Clean Technology
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    • v.25 no.1
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    • pp.91-97
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    • 2019
  • Oxidative desulfurization (ODS) has received much attention in recent years because refractory sulfur compounds such as dibenzothiophenes can be oxidized selectively to their corresponding sulfoxides and sulfones, and these products can be removed by extraction and adsorption. In this work, The oxidative desulfurization of marine diesel fuel was performed in a batch reactor with hydrogen peroxide (H2O2) in the presence of various supported heteropoly acid catalysts. The catalysts were characterized by XRD, XRF, XPS and nitrogen adsorption isotherm techniques. Based on the sulfur removal efficiency of promising silica supported heteropoly acid catalysts, the ranking of catalytic activity was: 30H3PW12/SiO2 > 30H3PMo12/SiO2 > 30H4SiW12/SiO2, which appears to be related with their intrinsic acid strength. The 30H3PW12/SiO2 catalyst showed the highest initial sulfur removal efficiency of about 66% under reaction conditions of 30C, 0.025gmL1 (cat./oil), 1 h reaction time. However, through the recycle test of the H3PW12/SiO2 catalyst, significant deactivation was observed, which was attributed to the elution of the active component H3PW12. By introducing cesium cation (Cs+) into the H3PW12/SiO2 catalyst, the stability of the catalyst was improved with changing the solubility, and the Cs+ ion exchanged catalyst could be recycled for at least five times without severe elution.

A Study on the Correlation between Uniaxial Compressive Strength of Rock by Elastic Wave Velocity and Elastic Modulus of Granite in Seoul and Gyeonggi Region (서울·경기지역 화강암의 탄성파속도와 탄성계수에 의한 암석의 일축압축강도와의 상관성 연구)

  • Son, In-Hwan;Kim, Byong-kuk;Lee, Byok-Kyu;Jang, Seung-jin;Lee, Su-Gon
    • Journal of the Society of Disaster Information
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    • v.15 no.2
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    • pp.249-258
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    • 2019
  • Purpose: The purpose of this study is to attain the correlation analysis and thereby to deduce the uniaxial compressive strength of rock specimens through the elastic wave velocity and the elastic modulus among the physical characteristics measured from the rock specimens collected during drilling investigations in Seoul and Gyeonggi region. Method: Experiments were conducted in the laboratory with 119 granite specimens in order to derive the correlation between the compressive strength of the rocks and elastic wave velocity and elastic modulus. Results: In the case of granite, the results of the analysis of the interaction between the compressive strength of a rock and the elastic wave velocity and elastic modulus were found to be less reliable in the relation equation as a whole. And it is believed that the estimation of the compressive strength by the elastic wave velocity and elastic modulus is less used because of the composition of non-homogeneous particles of granite. Conclusion: In this study, the analysis of correlation between the compressive strength of a rock and the elastic wave velocity and elastic modulus was performed with simple regression analysis and multiple regression analysis. The coefficient determination (R2) of simple regression analysis was shown between 0.61 and 0.67. Multiple regression analysis was 0.71. Thus, using multiple regression analysis when estimating compressive strength can increase the reliability of the correlation. Also, in the future, a variety of statistical analysis techniques such as recovery analysis, and artificial neural network analysis, and big data analysis can lead to more reliable results when estimating the compressive sterength of a rock based on the elastic wave velocity and elastic modulus.

The Effect of Data Size on the k-NN Predictability: Application to Samsung Electronics Stock Market Prediction (데이터 크기에 따른 k-NN의 예측력 연구: 삼성전자주가를 사례로)

  • Chun, Se-Hak
    • Journal of Intelligence and Information Systems
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    • v.25 no.3
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    • pp.239-251
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    • 2019
  • Statistical methods such as moving averages, Kalman filtering, exponential smoothing, regression analysis, and ARIMA (autoregressive integrated moving average) have been used for stock market predictions. However, these statistical methods have not produced superior performances. In recent years, machine learning techniques have been widely used in stock market predictions, including artificial neural network, SVM, and genetic algorithm. In particular, a case-based reasoning method, known as k-nearest neighbor is also widely used for stock price prediction. Case based reasoning retrieves several similar cases from previous cases when a new problem occurs, and combines the class labels of similar cases to create a classification for the new problem. However, case based reasoning has some problems. First, case based reasoning has a tendency to search for a fixed number of neighbors in the observation space and always selects the same number of neighbors rather than the best similar neighbors for the target case. So, case based reasoning may have to take into account more cases even when there are fewer cases applicable depending on the subject. Second, case based reasoning may select neighbors that are far away from the target case. Thus, case based reasoning does not guarantee an optimal pseudo-neighborhood for various target cases, and the predictability can be degraded due to a deviation from the desired similar neighbor. This paper examines how the size of learning data affects stock price predictability through k-nearest neighbor and compares the predictability of k-nearest neighbor with the random walk model according to the size of the learning data and the number of neighbors. In this study, Samsung electronics stock prices were predicted by dividing the learning dataset into two types. For the prediction of next day's closing price, we used four variables: opening value, daily high, daily low, and daily close. In the first experiment, data from January 1, 2000 to December 31, 2017 were used for the learning process. In the second experiment, data from January 1, 2015 to December 31, 2017 were used for the learning process. The test data is from January 1, 2018 to August 31, 2018 for both experiments. We compared the performance of k-NN with the random walk model using the two learning dataset. The mean absolute percentage error (MAPE) was 1.3497 for the random walk model and 1.3570 for the k-NN for the first experiment when the learning data was small. However, the mean absolute percentage error (MAPE) for the random walk model was 1.3497 and the k-NN was 1.2928 for the second experiment when the learning data was large. These results show that the prediction power when more learning data are used is higher than when less learning data are used. Also, this paper shows that k-NN generally produces a better predictive power than random walk model for larger learning datasets and does not when the learning dataset is relatively small. Future studies need to consider macroeconomic variables related to stock price forecasting including opening price, low price, high price, and closing price. Also, to produce better results, it is recommended that the k-nearest neighbor needs to find nearest neighbors using the second step filtering method considering fundamental economic variables as well as a sufficient amount of learning data.

Anomaly Detection for User Action with Generative Adversarial Networks (적대적 생성 모델을 활용한 사용자 행위 이상 탐지 방법)

  • Choi, Nam woong;Kim, Wooju
    • Journal of Intelligence and Information Systems
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    • v.25 no.3
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    • pp.43-62
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    • 2019
  • At one time, the anomaly detection sector dominated the method of determining whether there was an abnormality based on the statistics derived from specific data. This methodology was possible because the dimension of the data was simple in the past, so the classical statistical method could work effectively. However, as the characteristics of data have changed complexly in the era of big data, it has become more difficult to accurately analyze and predict the data that occurs throughout the industry in the conventional way. Therefore, SVM and Decision Tree based supervised learning algorithms were used. However, there is peculiarity that supervised learning based model can only accurately predict the test data, when the number of classes is equal to the number of normal classes and most of the data generated in the industry has unbalanced data class. Therefore, the predicted results are not always valid when supervised learning model is applied. In order to overcome these drawbacks, many studies now use the unsupervised learning-based model that is not influenced by class distribution, such as autoencoder or generative adversarial networks. In this paper, we propose a method to detect anomalies using generative adversarial networks. AnoGAN, introduced in the study of Thomas et al (2017), is a classification model that performs abnormal detection of medical images. It was composed of a Convolution Neural Net and was used in the field of detection. On the other hand, sequencing data abnormality detection using generative adversarial network is a lack of research papers compared to image data. Of course, in Li et al (2018), a study by Li et al (LSTM), a type of recurrent neural network, has proposed a model to classify the abnormities of numerical sequence data, but it has not been used for categorical sequence data, as well as feature matching method applied by salans et al.(2016). So it suggests that there are a number of studies to be tried on in the ideal classification of sequence data through a generative adversarial Network. In order to learn the sequence data, the structure of the generative adversarial networks is composed of LSTM, and the 2 stacked-LSTM of the generator is composed of 32-dim hidden unit layers and 64-dim hidden unit layers. The LSTM of the discriminator consists of 64-dim hidden unit layer were used. In the process of deriving abnormal scores from existing paper of Anomaly Detection for Sequence data, entropy values of probability of actual data are used in the process of deriving abnormal scores. but in this paper, as mentioned earlier, abnormal scores have been derived by using feature matching techniques. In addition, the process of optimizing latent variables was designed with LSTM to improve model performance. The modified form of generative adversarial model was more accurate in all experiments than the autoencoder in terms of precision and was approximately 7% higher in accuracy. In terms of Robustness, Generative adversarial networks also performed better than autoencoder. Because generative adversarial networks can learn data distribution from real categorical sequence data, Unaffected by a single normal data. But autoencoder is not. Result of Robustness test showed that he accuracy of the autocoder was 92%, the accuracy of the hostile neural network was 96%, and in terms of sensitivity, the autocoder was 40% and the hostile neural network was 51%. In this paper, experiments have also been conducted to show how much performance changes due to differences in the optimization structure of potential variables. As a result, the level of 1% was improved in terms of sensitivity. These results suggest that it presented a new perspective on optimizing latent variable that were relatively insignificant.

Development and evaluation of a 2-dimensional land surface flood analysis model using uniform square grid (정형 사각 격자 기반의 2차원 지표면 침수해석 모형 개발 및 평가)

  • Choi, Yun-Seok;Kim, Joo-Hun;Choi, Cheon-Kyu;Kim, Kyung-Tak
    • Journal of Korea Water Resources Association
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    • v.52 no.5
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    • pp.361-372
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    • 2019
  • The purpose of this study is to develop a two-dimensional land surface flood analysis model based on uniform square grid using the governing equations except for the convective acceleration term in the momentum equation. Finite volume method and implicit method were applied to spatial and temporal discretization. In order to reduce the execution time of the model, parallel computation techniques using CPU were applied. To verify the developed model, the model was compared with the analytical solution and the behavior of the model was evaluated through numerical experiments in the virtual domain. In addition, inundation analyzes were performed at different spatial resolutions for the domestic Janghowon area and the Sebou river area in Morocco, and the results were compared with the analysis results using the CAESER-LISFLOOD (CLF) model. In model verification, simulation results were well matched with the analytical solution, and the flow analyses in the virtual domain were also evaluated to be reasonable. The results of inundation simulations in the Janghowon and the Sebou river area by this study and CLF model were similar with each other and for Janghowon area, the simulation result was also similar to the flooding area of flood hazard map. The different parts in the simulation results of this study and the CLF model were compared and evaluated for each case. The results of this study suggest that the model proposed in this study can simulate the flooding well in the floodplain. However, in case of flood analysis using the model presented in this study, the characteristics and limitations of the model by domain composition method, governing equation and numerical method should be fully considered.

Effects of 2-methoxy-1,4-naphthoquinone (MQ) on MCP-1 Induced THP-1 Migration (MCP-1에 의해 유도된 THP-1 유주에 미치는 2-methoxy-1,4-naphthoquinone (MQ)의 영향)

  • Kim, Si Hyun;Park, Bo Bin;Hong, Sung Eun;Ryu, Sung Ryul;Lee, Jang Ho;Kim, Sa Hyun;Lee, Pyeongjae;Cho, Eun-Kyung;Moon, Cheol
    • Korean Journal of Clinical Laboratory Science
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    • v.51 no.2
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    • pp.245-251
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    • 2019
  • This study examined the effects of 2-methoxy-1,4-naphthoquinone (MQ) on the monocyte chemoattractant protein-1 (MCP-1)-induced migration of monocytes, which is an important phenomenon for the body defense and immune response. MQ is a major component extracted from Impatiens balsamina leaves, which have been used for many years in Asian medicine for the treatment of a range of diseases and pain. The cytotoxicity of MQ began to appear at a concentration of 10μM, and approximately 50% cytotoxicity was confirmed at 100μM. The MCP-1 induced migration of the THP-1 monocyte cell line increased after MQ treatment in a dose dependent manner and the largest increase was observed at 0.1μM. The level of cAMP expression decreased after a treatment with 0.1μM MQ. The phosphorylation of extracellular signal-regulated kinases 1/2 (Erk1/2), a key signaling protein involved in the signaling pathway of C-C motif chemokine receptor 2 (CCR2), a receptor for MCP-1, was increased by the simultaneous treatment of 0.1μM MQ. These results show that MQ increases the MCP-1-induced migration of THP-1, decreases the level of cAMP expression, and increases the level of Erk1/2 phosphorylation.

A Study on the Painting's Aesthetic of Gongjae Yoon Duseo (공재(恭齋) 윤두서(尹斗緖)의 회화심미(繪畵審美) 고찰)

  • Kim, Doyoung
    • The Journal of the Convergence on Culture Technology
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    • v.7 no.1
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    • pp.175-183
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    • 2021
  • Gongjae Yoon DuSeo(1668~1715), from Haenam in the late Joseon Dynasty, is a scholar-born painter who was active during King Sukjong. He is the person who created the foundation as a pioneer of realist paintings in the late Joseon period during the transition from the middle to the latter period. He was born in Namin's prestigious family, but he ended his career as part of a partisan fight and immersed himself in painting and learning. 18C, the beginning of the late Joseon Dynasty, was a period when Silhak emerged and the Jinkyung era opened with awareness of nationalism. At this time, by incorporating the Silhak thought into the art world, the real reformed aesthetic consciousness was demonstrated to pioneer common people's customs, the application of Western painting methods, the pursuit of realist techniques, and the introduction of Namjongmuninhwa. His view of painting, who thoroughly learned the old things and pursued change, must have both the form and spirit that he can achieve 'HwaDo' only when it has the science of 'learning and knowledge' and the technical elements of 'practice and quality' emphasized. He has worked in a variety of reconciliations. In particular, portrait paintings are characterized by ihyeongsasin's realistic expressions of aesthetics. His masterpiece, 「Self-portrait」, excels in extreme-realistic depiction and innovation in composition, and stands out with an unconventional experimentation spirit that expresses his mind and thoughts in a painting with a sense of resentment. His landscape paintings combine to express the form as it is and mental notions, and beautifully embodied Do as a form, thus achieving ihyeongmido, which reached the level of'joyfulness forgotten even the heart of joy'. On the other hand, the generalization of the common people using various common people's lives as the subject of an open-mindedness aimed at gaining the facts of ihyeongsajin, a passive protest against corrupt power and an expression of a spirit of love. Since then, his painting style has been passed down from generation to generation to his eldest son Yoon Deok-hee and his grandson Yoon Yong, leading the change and revival of calligraphy art in the late Joseon Dynasty.

Optimizing In Vitro Propagation of Sophora koreensis Nakai using Statistical Analysis (다양한 통계분석 기법을 이용한 개느삼(Sophora koreensis Nakai)의 기내 증식 최적 조건 구명)

  • Jeong, Ukhan;Lee, Hwa;Park, Sanghee;Cheong, Eun Ju
    • Journal of Korean Society of Forest Science
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    • v.110 no.1
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    • pp.53-63
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    • 2021
  • Sophora koreensis Nakai is an indigenous plant in Koreawith a restricted natural range, part of which is in Gangwon province. The species is known to contain phytochemicals that have beneficial effects on human health, and it is economically important in bioindustry. Because of the limited number of plants in a small range of habitats, the mass-propagation method should be developed for use and conservation. In vitro tissue culture is a reliable method in terms of mass propagation from selected clones of the species. We investigated the optimal conditions of the medium in this process, especially focusing on the concentrations of plant growth regulators(PGRs) in the culture of stem-containing axillary buds. Three statistical methods, i.e., ANOVA, response surface method(RSM), and fuzzy clustering were used to analyze the plant growth, number of shoots induced, and shoot length with various combinations of PGRs. Results from the RSM differed from those of the other two methods; thus, the method was not suitable. ANOVA and fuzzy clustering showed similar results. However, more accurate results were obtained using fuzzy clustering because it provided a probability for each treatment. On the basis of the fuzzy clustering analysis, stem tissue produced the greatest number of shoots(11.03 per explant; 63.33%) on a medium supplemented with 5-��M 6-benzylaminopurine and 2.5-��M thidiazuron(TDZ). Proliferation of shoots(2.18 ± 0.21 cm, 63.33%) was attained on a medium supplemented with 2.5-��M BA, 2.5-��M TDZ, and 2.5-��M gibberellic acid.

LSTM Based Prediction of Ocean Mixed Layer Temperature Using Meteorological Data (기상 데이터를 활용한 LSTM 기반의 해양 혼합층 수온 예측)

  • Ko, Kwan-Seob;Kim, Young-Won;Byeon, Seong-Hyeon;Lee, Soo-Jin
    • Korean Journal of Remote Sensing
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    • v.37 no.3
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    • pp.603-614
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    • 2021
  • Recently, the surface temperature in the seas around Korea has been continuously rising. This temperature rise causes changes in fishery resources and affects leisure activities such as fishing. In particular, high temperatures lead to the occurrence of red tides, causing severe damage to ocean industries such as aquaculture. Meanwhile, changes in sea temperature are closely related to military operation to detect submarines. This is because the degree of diffraction, refraction, or reflection of sound waves used to detect submarines varies depending on the ocean mixed layer. Currently, research on the prediction of changes in sea water temperature is being actively conducted. However, existing research is focused on predicting only the surface temperature of the ocean, so it is difficult to identify fishery resources according to depth and apply them to military operations such as submarine detection. Therefore, in this study, we predicted the temperature of the ocean mixed layer at a depth of 38m by using temperature data for each water depth in the upper mixed layer and meteorological data such as temperature, atmospheric pressure, and sunlight that are related to the surface temperature. The data used are meteorological data and sea temperature data by water depth observed from 2016 to 2020 at the IEODO Ocean Research Station. In order to increase the accuracy and efficiency of prediction, LSTM (Long Short-Term Memory), which is known to be suitable for time series data among deep learning techniques, was used. As a result of the experiment, in the daily prediction, the RMSE (Root Mean Square Error) of the model using temperature, atmospheric pressure, and sunlight data together was 0.473. On the other hand, the RMSE of the model using only the surface temperature was 0.631. These results confirm that the model using meteorological data together shows better performance in predicting the temperature of the upper ocean mixed layer.

Water Digital Twin for High-tech Electronics Industrial Wastewater Treatment System (II): e-ASM Calibration, Effluent Prediction, Process selection, and Design (첨단 전자산업 폐수처리시설의 Water Digital Twin(II): e-ASM 모델 보정, 수질 예측, 공정 선택과 설계)

  • Heo, SungKu;Jeong, Chanhyeok;Lee, Nahui;Shim, Yerim;Woo, TaeYong;Kim, JeongIn;Yoo, ChangKyoo
    • Clean Technology
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    • v.28 no.1
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    • pp.79-93
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    • 2022
  • In this study, an electronics industrial wastewater activated sludge model (e-ASM) to be used as a Water Digital Twin was calibrated based on real high-tech electronics industrial wastewater treatment measurements from lab-scale and pilot-scale reactors, and examined for its treatment performance, effluent quality prediction, and optimal process selection. For specialized modeling of a high-tech electronics industrial wastewater treatment system, the kinetic parameters of the e-ASM were identified by a sensitivity analysis and calibrated by the multiple response surface method (MRS). The calibrated e-ASM showed a high compatibility of more than 90% with the experimental data from the lab-scale and pilot-scale processes. Four electronics industrial wastewater treatment processes-MLE, A2/O, 4-stage MLE-MBR, and Bardenpo-MBR-were implemented with the proposed Water Digital Twin to compare their removal efficiencies according to various electronics industrial wastewater characteristics. Bardenpo-MBR stably removed more than 90% of the chemical oxygen demand (COD) and showed the highest nitrogen removal efficiency. Furthermore, a high concentration of 1,800 mg L-1 T MAH influent could be 98% removed when the HRT of the Bardenpho-MBR process was more than 3 days. Hence, it is expected that the e-ASM in this study can be used as a Water Digital Twin platform with high compatibility in a variety of situations, including plant optimization, Water AI, and the selection of best available technology (BAT) for a sustainable high-tech electronics industry.