• Clean Technology
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• v.27 no.2
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• pp.182-189
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• 2021

The adsorption of disperse yellow 3 (DY 3) on granular activated carbon (GAC) was investigated for isothermal adsorption and kinetic and thermodynamic parameters by experimenting with initial concentration, contact time, temperature, and pH of the dye as adsorption parameters. In the pH change experiment, the adsorption percent of DY 3 on activated carbon was highest in the acidic region, pH 3 due to electrostatic attraction between the surface of the activated carbon with positive charge and the anion (OH^-) of DY 3. The adsorption equilibrium data of DY 3 fit the Langmuir isothermal adsorption equation best, and it was found that activated carbon can effectively remove DY 3 from the calculated separation factor (R_L). The heat of adsorption-related constant (B) from the Temkin equation did not exceed 20 J mol^-1, indicating that it is a physical adsorption process. The pseudo second order kinetic model fits well within 10.72% of the error percent in the kinetic experiments. The plots for Weber and Morris intraparticle diffusion model were divided into two straight lines. The intraparticle diffusion rate was slow because the slope of the stage 2 (intraparticle diffusion) was smaller than that of stage 1 (boundary layer diffusion). Therefore, it was confirmed that the intraparticle diffusion was rate controlling step. The free energy change of the DY 3 adsorption by activated carbon showed negative values at 298 ~ 318 K. As the temperature increased, the spontaneity increased. The enthalpy change of the adsorption reaction of DY 3 by activated carbon was 0.65 kJ mol^-1, which was an endothermic reaction, and the entropy change was 2.14 J mol^-1 K^-1.

• Journal of Life Science
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• v.32 no.2
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• pp.161-166
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• 2022

Mitosis is a process in which a replicated genome is distributed to two daughter cells, and it is necessary for cell survival and organismal development. During mitosis, the spindle assembly checkpoint (SAC) ensures faithful chromosome segregation by monitoring the kinetochore attachment to the mitotic spindle. Although the SAC mechanism has been extensively studied over the last 30 years, the mechanism by which a single unattached kinetochore activates the SAC remains unclear. The key components of the SAC are Mad1, Mad2, Mad3 (BubR1 in higher eukaryotes), Bub1, Bub3, and Cdc20, which are all required for SAC activation. An essential step for SAC activation is the formation of the Mad2 - Cdc20 complex in the unattached kinetochore, which is kinetically disfavored. Although the mechanism by which Mad2 and Cdc20 are recruited to unattached kinetochores is well-known, it is not clear how they form a complex. Recently, a key mechanism for the formation of the Mad2 - Cdc20 complex has been identified, which is catalyzed by an unattached kinetochore. This supports the evidence that a single unattached kinetochore can activate the SAC signaling. Herein, we discuss the known key mechanism for SAC activation, review the recent studies on SAC, and conclude how their discoveries improved the understanding of mitosis.

• Journal of the Korean Society for Library and Information Science
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• v.56 no.3
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• pp.241-264
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• 2022

Bibliographic metadata can help researchers effectively utilize essential publications that they need and grasp academic trends of their own fields. With the manual creation of the metadata costly and time-consuming. it is nontrivial to effectively automatize the metadata construction using rule-based methods due to the immoderate variety of the article forms and styles according to publishers and academic societies. Therefore, this study proposes a two-step extraction process based on rules and deep neural networks for generating bibliographic metadata of scientific articlles to overcome the difficulties above. The extraction target areas in articles were identified by using a deep neural network-based model, and then the details in the areas were analyzed and sub-divided into relevant metadata elements. IThe proposed model also includes a model for generating reference summary information, which is able to separate the end of the text and the starting point of a reference, and to extract individual references by essential rule set, and to identify all the bibliographic items in each reference by a deep neural network. In addition, in order to confirm the possibility of a model that generates the bibliographic information of academic papers without pre- and post-processing, we conducted an in-depth comparative experiment with various settings and configurations. As a result of the experiment, the method proposed in this paper showed higher performance.

• Clean Technology
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• v.28 no.3
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• pp.210-217
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• 2022

Boron(B) is a rare resource used for various purposes such as glass, semiconductor materials, gunpowder, rocket fuel, etc. However, Korea depends entirely on imports for boron. Considering the global boron reserves and its current production rate, boron will be depleted on earth in 50 years. Thus, a process including proper adsorbent materials recovering boron from seawater is demanded. This research proposed carbonized nanofibers prepared from electrospun PAN(polyacrylonitrile) mats as promising materials to adsorb boron in aqueous solution. First, the mechanism of boron adsorption on carbonized nanofibers was investigated by DFT(density functional method)-based molecular modeling and the calculated energetics demonstrated that the boron chemisorption on the nitrogen-doped graphene surface by a two-step dehydration is possible with viable activation energies. Then, the electrospun PAN mats were stabilized in air and then carbonized in an argon atmosphere before being immersed in the boric acid aqueous solution. Analytically, SEM(scanning electron microscopy) and Raman measurements were employed to confirm whether the electrospinning and carbonization of PAN mats proceeded successfully. Then, XPS(X-ray photoelectron spectroscopy) peak analysis showed whether the intended nitrogen-doped carbon nanofiber surface was formed and boron was properly adsorbed on nanofibers. Those results demonstrated that the carbonized nanofibers prepared from electrospun PAN mats could be feasible adsorbents for boron recovery in seawater.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.35 no.1
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• pp.44-49
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• 2022

In this paper, we investigated current (I)- and voltage (V)-sweeping properties in a double-stack structure, Ge₂Sb₂Te₅/Ti/W-doped Ge₈Sb₂Te₁₁, a candidate medium for applications to multilevel phase-change memory. 200-nm-thick and W-doped Ge₂Sb₂Te₅ and W-doped Ge₈Sb₂Te₁₁ films were deposited on p-type Si(100) substrate using magnetron sputtering system, and the sheet resistance was measured using 4 point-probe method. The sheet resistance of amorphous-phase W-doped Ge₈Sb₂Te₁₁ film was about 1 order larger than that of Ge₂Sb₂Te₅ film. The I- and V-sweeping properties were measured using sourcemeter, pulse generator, and digital multimeter. The speed of amorphous-to-multilevel crystallization was evaluated from a graph of resistance vs. pulse duration (t) at a fixed applied voltage (12 V). All the double-stack cells exhibited a two-step phase change process with the multilevel memory states of high-middle-low resistance (HR-MR-LR). In particular, the stable MR state is required to guarantee the reliability of the multilevel phase-change memory. For the Ge₂Sb₂Te₅ (150 nm)/Ti (20 nm)/W-Ge₈Sb₂Te₁₁ (50 nm), the phase transformations of HR→MR and MR→LR were observed at t<30ns and t<65ns, respectively. We believe that a high speed and stable multilevel phase-change memory can be optimized by the double-stack structure of proper Ge-Sb-Te films separated by a barrier metal (Ti).

• Informatization Policy
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• v.29 no.3
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• pp.26-47
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• 2022

"The certification of compliance of new products of industrial convergence" (hereinafter referred to as "certification of compliance") is a legal certification system in accordance with the Industrial Convergence Promotion Act through which a convergence new product can be officially certified without legislation when the certification standards applicable to the product are not yet provided. Unlike other certification systems, the certification of compliance is characterized by the role of resolving the certification difficulties driven by the regulatory lag of convergence new products. Nevertheless, studies that analyzed the certification of compliance in detail from the viewpoint of regulatory improvement were surprisingly rare. Through the sequential matching of the steps of certification of compliance with the process from the occurrence of a regulatory problem to resolution, our study provided clear understanding as to how the regulatory lag could be reduced by the procedure for certification of compliance. Furthermore, we divided the perspective on regulatory lag management into quantitative and qualitative, and the structures and practices of certification of compliance were then analyzed from the two perspectives. By doing this, the present study emphasized that the fundamental reason the certification of compliance could effectively solve the regulatory lag problem of convergence new products was not only the quantitative elements such as legal deadlines for each step but also several qualitative approaches to securing the quality of every stage.

• Geophysics and Geophysical Exploration
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• v.25 no.3
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• pp.140-161
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• 2022

Seismic data with missing traces are often obtained regularly or irregularly due to environmental and economic constraints in their acquisition. Accordingly, seismic data interpolation is an essential step in seismic data processing. Recently, research activity on machine learning-based seismic data interpolation has been flourishing. In particular, convolutional neural network (CNN) and generative adversarial network (GAN), which are widely used algorithms for super-resolution problem solving in the image processing field, are also used for seismic data interpolation. In this study, CNN-based algorithm, U-Net and GAN-based algorithm, and conditional Wasserstein GAN (cWGAN) were used as seismic data interpolation methods. The results and performances of the methods were evaluated thoroughly to find an optimal interpolation method, which reconstructs with high accuracy missing seismic data. The work process for model training and performance evaluation was divided into two cases (i.e., Cases I and II). In Case I, we trained the model using only the regularly sampled data with 50% missing traces. We evaluated the model performance by applying the trained model to a total of six different test datasets, which consisted of a combination of regular, irregular, and sampling ratios. In Case II, six different models were generated using the training datasets sampled in the same way as the six test datasets. The models were applied to the same test datasets used in Case I to compare the results. We found that cWGAN showed better prediction performance than U-Net with higher PSNR and SSIM. However, cWGAN generated additional noise to the prediction results; thus, an ensemble technique was performed to remove the noise and improve the accuracy. The cWGAN ensemble model removed successfully the noise and showed improved PSNR and SSIM compared with existing individual models.

• (The) Research of the performance art and culture
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• no.39
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• pp.581-607
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• 2019

Noting that exorcism play and mask play are different in their ritual nature, this paper aims to examine their ritual through the social drama theory of Victor Turner, a cultural anthropologist. Turner views every incident in human history as a social drama and interprets it based on the four-step structural theory of breach, crisis, redressive action, and reintegration. In particular, he believes that the redressive phase takes place through a ritual solution rather than a legal or political solution in the village community. Based on such Turner's theory, Chapter 2 analyzes Yeonggamnori, Jeju's typical exorcism play, and explains the process leading to reintegration in accordance with peaceful ritual. Chapter 3 then analyzes the Puppet Play on the same principle and examines that redressive action is being resolved through a sacrificial ritual in the case of this play. Chapter 4 checks whether the results from the previous two plays show similar aspects in other traditional plays. To this end, the exorcism play will be analyzed for Jeju's Seocheon Flower Play, Junsangnori, Segyeongnori and Sanshinnori, while the mask play will include Bongsan Mask Dance, Yangju Byeonsandae Play, Goseong Ogwangdae and Hahoe Mask Dance. As a result of these studies, it is the main point of the study to prove that exorcism play and mask play are different in their ritual nature. However, this research is only in the stage of seeking differences in its ritual, and the review on the historical and social causes of differences is left as a research task at a later date.

• 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.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1505-1514
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• 2022

Image matching is a crucial preprocessing step for effective utilization of multi-temporal and multi-sensor very high resolution (VHR) satellite images. Deep learning (DL) method which is attracting widespread interest has proven to be an efficient approach to measure the similarity between image pairs in quick and accurate manner by extracting complex and detailed features from satellite images. However, Image matching of VHR satellite images remains challenging due to limitations of DL models in which the results are depending on the quantity and quality of training dataset, as well as the difficulty of creating training dataset with VHR satellite images. Therefore, this study examines the feasibility of DL-based method in matching pair extraction which is the most time-consuming process during image registration. This paper also aims to analyze factors that affect the accuracy based on the configuration of training dataset, when developing training dataset from existing multi-sensor VHR image database with bias for DL-based image matching. For this purpose, the generated training dataset were composed of correct matching pairs and incorrect matching pairs by assigning true and false labels to image pairs extracted using a grid-based Scale Invariant Feature Transform (SIFT) algorithm for a total of 12 multi-temporal and multi-sensor VHR images. The Siamese convolutional neural network (SCNN), proposed for matching pair extraction on constructed training dataset, proceeds with model learning and measures similarities by passing two images in parallel to the two identical convolutional neural network structures. The results from this study confirm that data acquired from VHR satellite image database can be used as DL training dataset and indicate the potential to improve efficiency of the matching process by appropriate configuration of multi-sensor images. DL-based image matching techniques using multi-sensor VHR satellite images are expected to replace existing manual-based feature extraction methods based on its stable performance, thus further develop into an integrated DL-based image registration framework.