• Korean Journal of Heritage: History & Science
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• v.55 no.2
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• pp.82-100
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• 2022

With the crisis of the COVID-19 pandemic and the era of the 4th industrial revolution, archaeological heritage education has entered a new phase. This article responds to the trends in the post-COVID-19 era, seeking ways to develop archaeological heritage education and sustainable strategies necessary in the era of the 4th industrial revolution. The program of archaeological heritage education required in the era of the 4th industrial revolution must cultivate creative talent, solve problems, and improve self-efficacy. It should also draw attention to archaeological heritage maker education. Such maker education should be delivered based on constructivism and be designed by setting specific learning goals in consideration of various age-specific characteristics. Moreover, various ICT-based contents applying VR, AR, cloud, and drone imaging technologies should be developed and expanded, and, above all, ontact digital education(real-time virtual learning) should seek ways to revitalize communities capable of interactive communication in non-face-to-face situations. The development of such ancient heritage content needs to add AI functions that consider learners' interests, learning abilities, and learning purposes while producing various convergent contents from the standpoint of "cultural collage." Online archaeological heritage content education should be delivered following prior learning or with supplementary learning in consideration of motivation or field learning to access the real thing in the future. Ultimately, archaeological ontact education will be delivered using cutting-edge technologies that reflect the current trends. In conjunction with this, continuous efforts are needed for constructive learning that enables discovery and question-exploration.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.191-206
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• 2022

Recently, it is a de-facto approach to utilize a pre-trained language model(PLM) to achieve the state-of-the-art performance for various natural language tasks(called downstream tasks) such as sentiment analysis and question answering. However, similar to any other machine learning method, PLM tends to depend on the data distribution seen during the training phase and shows worse performance on the unseen (Out-of-Distribution) domain. Due to the aforementioned reason, there have been many efforts to develop domain-specified PLM for various fields such as medical and legal industries. In this paper, we discuss the training of a finance domain-specified PLM for the Korean language and its applications. Our finance domain-specified PLM, KB-BERT, is trained on a carefully curated financial corpus that includes domain-specific documents such as financial reports. We provide extensive performance evaluation results on three natural language tasks, topic classification, sentiment analysis, and question answering. Compared to the state-of-the-art Korean PLM models such as KoELECTRA and KLUE-RoBERTa, KB-BERT shows comparable performance on general datasets based on common corpora like Wikipedia and news articles. Moreover, KB-BERT outperforms compared models on finance domain datasets that require finance-specific knowledge to solve given problems.

• Resources Recycling
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• v.31 no.4
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• pp.56-65
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• 2022

Copper is one of the non-ferrous metals used in the electrical/electronic manufacturing industries due to its superior properties particularly the high conductivity and less resistivity. The effluent generated from the surface finishing process of these industries contains higher copper content which gets discharged in to water bodies directly or indirectly. This causes severe environmental pollution and also results in loss of an important valuable metal. To overcome this issue, continuous R & D activities are going on across the globe in adsorption area with the purpose of finding an efficient, low cost and ecofriendly adsorbent. In view of the above, present investigation was made to compare the performance of a plant root (Datura root powder) as a bio-adsorbent to that of the synthetic one (Tulsion T-42) for copper adsorption from such effluent. Experiments were carried out in batch studies to optimize parameters such as adsorbent dose, contact time, pH, feed concentration, etc. Results of the batch experiments indicate that 0.2 g of Datura root powder and 0.1 g of Tulsion T-42 showed 95% copper adsorption from an initial feed/solution of 100 ppm Cu at pH 4 in contact time of 15 and 30 min, respectively. Adsorption data for both the adsorbents were fitted well to the Freundlich isotherm. Experimental results were also validated with the kinetic model, which showed that the adsorption of copper followed pseudo-second order rate expression for the both adsorbents. Overall result demonstrates that the bio-adsorbent tested has a potential applicability for metal recovery from the waste solutions/effluents of metal finishing units. In view of the requirements of commercial viability and minimal environmental damage there from, Datura root powder being an effective material for metal uptake, may prove to be a feasible adsorbent for copper recovery after the necessary scale-up studies.

• Environmental and Resource Economics Review
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• v.31 no.2
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• pp.165-204
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• 2022

This study organized data from 2000 to 2014 for 20 grid-connected countries in Europe and analyzed the determinants of carbon emissions through the panel GLS method considering the problem of heteroscedasticity and autocorrelation. At the same time, the effect of introducing ETS was considered by dividing the sample period as of 2005 when the European emission trading system was introduced. Carbon emissions from individual countries were used as dependent variables, and proportion of generation by each source, power self-sufficiency ratio of neighboring countries, power production from resource-holding countries, concentration of power sources, total energy consumption per capita in the industrial sector, tax of electricity, net electricity export per capita, and size of national territory per capita. According to the estimation results, the proportion of nuclear power and renewable energy generation, concentration of power sources, and size of the national territory area per capita had a negative (-) effect on carbon emissions both before and after 2005. On the other hand, the proportion of coal power generation, the power supply and demand rate of neighboring countries, the power production of resource-holding countries, and the total energy consumption per capita in the industrial sector were found to have a positive (+) effect on carbon emissions. In addition, the proportion of gas generation had a negative (-) effect on carbon emissions, and tax of electricity were found to have a positive (+) effect. However, all of these were only significant before 2005. It was found that net electricity export per capita had a negative (-) effect on carbon emissions only after 2005. The results of this study suggest macroscopic strategies to reduce carbon emissions to green growth, suggesting mid- to long-term power mix optimization measures considering the electricity trade market and their role.

• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.167-176
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• 2022

We designed a borehole deviation survey tool applicable for steel-cased holes, K-DEV, and developed a prototype for a depth of 500 m aiming to development of own equipment required to secure deep subsurface characterization technologies. K-DEV is equipped with sensors that provide digital output with verified high performance; moreover, it is also compatible with logging winch systems used in Korea. The K-DEV prototype has a nonmagnetic stainless steel housing with an outer diameter of 48.3 mm, which has been tested in the laboratory for water resistance up to 20 MPa and for durability by running into a 1-km deep borehole. We confirmed the operational stability and data repeatability of the prototype by constantly logging up and down to the depth of 600 m. A high-precision micro-electro-mechanical system (MEMS) gyroscope was used for the K-DEV prototype as the gyro sensor, which is crucial for azimuth determination in cased holes. Additionally, we devised an accurate trajectory survey algorithm by employing Unscented Kalman filtering and data fusion for optimization. The borehole test with K-DEV and a commercial logging tool produced sufficiently similar results. Furthermore, the issue of error accumulation due to drift over time of the MEMS gyro was successfully overcome by compensating with stationary measurements for the same attitude at the wellhead before and after logging, as demonstrated by the nearly identical result to the open hole. We believe that the methodology of K-DEV development and operational stability, as well as the data reliability of the prototype, were confirmed through these test applications.

• The Journal of Korean Society for Radiation Therapy
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• v.28 no.2
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• pp.171-178
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• 2016

Purpose : In order to minimize the dose of femoral head as an appropriate treatment plan for rectal cancer radiation therapy, we compare and evaluate the usefulness of 3-field 3D conformal radiation therapy(below 3fCRT), which is a universal treatment method, and 5-field 3D conformal radiation therapy(below 5fCRT), and Volumetric Modulated Arc Therapy (VMAT). Materials and Methods : The 10 cases of rectal cancer that treated with 21EX were enrolled. Those cases were planned by Eclipse(Ver. 10.0.42, Varian, USA), PRO3(Progressive Resolution Optimizer 10.0.28) and AAA(Anisotropic Analytic Algorithm Ver. 10.0.28). 3fCRT and 5fCRT plan has $0^{\circ}$, $270^{\circ}$, $90^{\circ}$ and $0^{\circ}$, $95^{\circ}$, $45^{\circ}$, $315^{\circ}$, $265^{\circ}$ gantry angle, respectively. VMAT plan parameters consisted of 15MV coplanar $360^{\circ}$ 1 arac. Treatment prescription was employed delivering 54Gy to recum in 30 fractions. To minimize the dose difference that shows up randomly on optimizing, VMAT plans were optimized and calculated twice, and normalized to the target V100%=95%. The indexes of evaluation are D of Both femoral head and aceta fossa, total MU, H.I.(Homogeneity index) and C.I.(Conformity index) of the PTV. All VMAT plans were verified by gamma test with portal dosimetry using EPID. Results : D of Rt. femoral head was 53.08 Gy, 50.27 Gy, and 30.92 Gy, respectively, in the order of 3fCRT, 5fCRT, and VMAT treatment plan. Likewise, Lt. Femoral head showed average 53.68 Gy, 51.01 Gy and 29.23 Gy in the same order. D of Rt. aceta fossa was 54.86 Gy, 52.40 Gy, 30.37 Gy, respectively, in the order of 3fCRT, 5fCRT, and VMAT treatment plan. Likewise, Lt. Femoral head showed average 53.68 Gy, 51.01 Gy and 29.23 Gy in the same order. The maximum dose of both femoral head and aceta fossa was higher in the order of 3fCRT, 5fCRT, and VMAT treatment plan. C.I. showed the lowest VMAT treatment plan with an average of 1.64, 1.48, and 0.99 in the order of 3fCRT, 5fCRT, and VMAT treatment plan. There was no significant difference on H.I. of the PTV among three plans. Total MU showed that the VMAT treatment plan used 124.4MU and 299MU more than the 3fCRT and 5fCRT treatment plan, respectively. IMRT verification gamma test results for the VMAT plan passed over 90.0% at 2mm/2%. Conclusion : In rectal cancer treatment, the VMAT plan was shown to be advantageous in most of the evaluation indexes compared to the 3D plan, and the dose of the femoral head was greatly reduced. However, because of practical limitations there may be a case where it is difficult to select a VMAT treatment plan. 5fCRT has the advantage of reducing the dose of the femoral head as compared to the existing 3fCRT, without regard to additional problems. Therefore, not only would it extend survival time but the quality of life in general, if hospitals improved radiation therapy efficiency by selecting the treatment plan in accordance with the hospital's situation.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.125-140
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• 2013

We call a data set in which the number of records belonging to a certain class far outnumbers the number of records belonging to the other class, 'imbalanced data set'. Most of the classification techniques perform poorly on imbalanced data sets. When we evaluate the performance of a certain classification technique, we need to measure not only 'accuracy' but also 'sensitivity' and 'specificity'. In a customer churn prediction problem, 'retention' records account for the majority class, and 'churn' records account for the minority class. Sensitivity measures the proportion of actual retentions which are correctly identified as such. Specificity measures the proportion of churns which are correctly identified as such. The poor performance of the classification techniques on imbalanced data sets is due to the low value of specificity. Many previous researches on imbalanced data sets employed 'oversampling' technique where members of the minority class are sampled more than those of the majority class in order to make a relatively balanced data set. When a classification model is constructed using this oversampled balanced data set, specificity can be improved but sensitivity will be decreased. In this research, we developed a hybrid model of support vector machine (SVM), artificial neural network (ANN) and decision tree, that improves specificity while maintaining sensitivity. We named this hybrid model 'hybrid SVM model.' The process of construction and prediction of our hybrid SVM model is as follows. By oversampling from the original imbalanced data set, a balanced data set is prepared. SVM_I model and ANN_I model are constructed using the imbalanced data set, and SVM_B model is constructed using the balanced data set. SVM_I model is superior in sensitivity and SVM_B model is superior in specificity. For a record on which both SVM_I model and SVM_B model make the same prediction, that prediction becomes the final solution. If they make different prediction, the final solution is determined by the discrimination rules obtained by ANN and decision tree. For a record on which SVM_I model and SVM_B model make different predictions, a decision tree model is constructed using ANN_I output value as input and actual retention or churn as target. We obtained the following two discrimination rules: 'IF ANN_I output value <0.285, THEN Final Solution = Retention' and 'IF ANN_I output value ${\geq}0.285$, THEN Final Solution = Churn.' The threshold 0.285 is the value optimized for the data used in this research. The result we present in this research is the structure or framework of our hybrid SVM model, not a specific threshold value such as 0.285. Therefore, the threshold value in the above discrimination rules can be changed to any value depending on the data. In order to evaluate the performance of our hybrid SVM model, we used the 'churn data set' in UCI Machine Learning Repository, that consists of 85% retention customers and 15% churn customers. Accuracy of the hybrid SVM model is 91.08% that is better than that of SVM_I model or SVM_B model. The points worth noticing here are its sensitivity, 95.02%, and specificity, 69.24%. The sensitivity of SVM_I model is 94.65%, and the specificity of SVM_B model is 67.00%. Therefore the hybrid SVM model developed in this research improves the specificity of SVM_B model while maintaining the sensitivity of SVM_I model.