• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.127-137
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• 2019

Recidivism prediction has been a subject of constant research by experts since the early 1970s. But it has become more important as committed crimes by recidivist steadily increase. Especially, in the 1990s, after the US and Canada adopted the 'Recidivism Risk Assessment Report' as a decisive criterion during trial and parole screening, research on recidivism prediction became more active. And in the same period, empirical studies on 'Recidivism Factors' were started even at Korea. Even though most recidivism prediction studies have so far focused on factors of recidivism or the accuracy of recidivism prediction, it is important to minimize the prediction misclassification cost, because recidivism prediction has an asymmetric error cost structure. In general, the cost of misrecognizing people who do not cause recidivism to cause recidivism is lower than the cost of incorrectly classifying people who would cause recidivism. Because the former increases only the additional monitoring costs, while the latter increases the amount of social, and economic costs. Therefore, in this paper, we propose an XGBoost(eXtream Gradient Boosting; XGB) based recidivism prediction model considering asymmetric error cost. In the first step of the model, XGB, being recognized as high performance ensemble method in the field of data mining, was applied. And the results of XGB were compared with various prediction models such as LOGIT(logistic regression analysis), DT(decision trees), ANN(artificial neural networks), and SVM(support vector machines). In the next step, the threshold is optimized to minimize the total misclassification cost, which is the weighted average of FNE(False Negative Error) and FPE(False Positive Error). To verify the usefulness of the model, the model was applied to a real recidivism prediction dataset. As a result, it was confirmed that the XGB model not only showed better prediction accuracy than other prediction models but also reduced the cost of misclassification most effectively.

• Korean Chemical Engineering Research
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• v.59 no.4
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• pp.574-583
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• 2021

Waste air containing ethanol (100 ppmv) and hydrogen sulfide (10 ppmv) was continuously treated by waste air-treating system composed of two annular photocatalytic reactors (effective volume: 1.5 L) packed with porous SiO₂ media carrying TiO₂-anatase photocatalyst, one of which was alternately operated for 32 d/run while the other was regenerated by 100 ℃ hot air with 15 W UV(-A)-light on. As its elimination-behavior of ethanol, the removal efficiencies of ethanol at 1^st, 2^nd and 3^rd operation of the photocatalytic reactor system(A), turned out to be ca. 60, 55 and 54%, respectively, at their steady state condition. Unlike the elimination-behavior of ethanol, its hydrogen sulfide-elimination behavior showed repeated decrease of hydrogen sulfide removal efficiency by its resultant arrival at a lower level of steady state condition. Nevertheless, the removal efficiencies of hydrogen sulfide at 1^st, 2^nd and 3^rd operation of the photocatalytic reactor system, turned out to be ca. 80, 75 and 73%, respectively, at their final steady state condition, higher by ca. 20, 20 and 19% than those of ethanol, respectively. Therefore, assuming that adsorption on porous SiO₂-photocatalyst carrier was regarded to belong to a reversible deactivation and that decreased % of removal efficiency due to the reversible deactivation of photocatalyst including the adsorption was independent of the number of its use upon regeneration, the increments of the decreased % of removal efficiency of ethanol and hydrogen sulfide, due to an irreversible deactivation of photocatalyst, for the 3^rd use of regenerated photocatalyst, compared with the 2^nd use of regenerated photocatalyst, were ca. 1 and 2%, respectively, which was insignificant or the less than those of ca. 5 and 5%, respectively, for the 2^nd use of regenerated photocatalyst compared with the 1^st use of virgin photocatalyst. This trend of the photocatalytic reactor system was observed to be similar to that of the other alternately-operating photocatalytic reactor system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.431-438
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• 2021

To prepare more stringent regulations for USCG Phase II ballast water management, this study investigated the staining efficiency of SYBR Green I(SGI) and SYBR Gold(SG) on the virus-like particle (VLP). A dye with high staining efficiency was applied to the treated water that was passed through the ballast water treatment system (BWTS). VLP staining was observed most clearly under the 100-fold and 200-fold dilution of the stock solution when the volume of filtered samples was 0.5 mL to 2 mL. The staining efficiency of SGI and SG did not show a significant difference. On the other hand, the green fluorescence of viruses in the sample stained with SGI was more pronounced than in the samples stained with SG (expressed yellow fluorescence), making it easier to observe. The abundance of VLP in the test water and control water treatments that did not pass through the two types of BWTS (electrolysis type, UV + electrolysis type) was approximately 109 - 1010 VLP 100 mL-1. In contrast, no stained VLP was observed in the treated water treatments. Moreover, SGI was confirmed to be effectively stained under various salinity conditions, including seawater, brackish water, and freshwater. Further verification tests and development of staining methods under various BWTS are required, but the SGI staining method is believed to be a good alternative to the VLP live/dead determination of the USCG Phase II type approval test.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1107-1118
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• 2021

Climate change brought on by global warming increased the frequency of flood and drought on the Korean Peninsula, along with the casualties and physical damage resulting therefrom. Preparation and response to these water disasters requires national-level planning for water resource management. In addition, watershed-level management of water resources requires flow duration curves (FDC) derived from continuous data based on long-term observations. Traditionally, in water resource studies, physical rainfall-runoff models are widely used to generate duration curves. However, a number of recent studies explored the use of data-based deep learning techniques for runoff prediction. Physical models produce hydraulically and hydrologically reliable results. However, these models require a high level of understanding and may also take longer to operate. On the other hand, data-based deep-learning techniques offer the benefit if less input data requirement and shorter operation time. However, the relationship between input and output data is processed in a black box, making it impossible to consider hydraulic and hydrological characteristics. This study chose one from each category. For the physical model, this study calculated long-term data without missing data using parameter calibration of the Soil Water Assessment Tool (SWAT), a physical model tested for its applicability in Korea and other countries. The data was used as training data for the Long Short-Term Memory (LSTM) data-based deep learning technique. An anlysis of the time-series data fond that, during the calibration period (2017-18), the Nash-Sutcliffe Efficiency (NSE) and the determinanation coefficient for fit comparison were high at 0.04 and 0.03, respectively, indicating that the SWAT results are superior to the LSTM results. In addition, the annual time-series data from the models were sorted in the descending order, and the resulting flow duration curves were compared with the duration curves based on the observed flow, and the NSE for the SWAT and the LSTM models were 0.95 and 0.91, respectively, and the determination coefficients were 0.96 and 0.92, respectively. The findings indicate that both models yield good performance. Even though the LSTM requires improved simulation accuracy in the low flow sections, the LSTM appears to be widely applicable to calculating flow duration curves for large basins that require longer time for model development and operation due to vast data input, and non-measured basins with insufficient input data.

• The Korean Society of Law and Medicine
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• v.21 no.2
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• pp.209-244
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• 2020

TMS and tDCS are non-invasive devices that treat the diseases of patients or individual users, and manage or improve their health by applying stimulation to a brain through magnetism and electricity. The effect and safety of these devices have proved to be valid in several diseases, but research in this area is still much going on. Despite increasing cases of their application, legislations directly regulating TMS and tDCS are hard to find. Legal regulation regarding TMS and tDCS in the United States, Germany and Japan reveals that while TMS has been approved as a medical device with a moderate risk, tDCS has not yet earned approval as a medical device. However, the recent FDA guidance, European MDR changes, recalls in the US, and relevant legal provisions of Germany and Japan, as well as recommendations from expert groups all show signs of tDCS growing closer to getting approved as a medical device. Of course, safety and efficacy of tDCS can still be regulated as a general product instead of as a medical device. Considering multiple potential impacts on a human brain, however, the need for independent regulation is urgent. South Korea also lacks legal provisions explicitly regulating TMS and tDCS, but they fall into the category of the grade 3 medical devices according to the notifications of the Korean Ministry of Food and Drug Safety. And safety and efficacy of TMS are to be evaluated in compliance with the US FDA guidance. But no specific guidelines exist for tDCS yet. Given that tDCS devices are used in some hospitals in reality, and also at home by individual buyers, such a regulatory gap must quickly be addressed. In a longer term, legal system needs to be in place capable of independently regulating non-invasive brain stimulating devices.

• Ecology and Resilient Infrastructure
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• v.9 no.3
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• pp.163-173
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• 2022

Climate change is a key factor that greatly influences changes in the biological seasons and geographical distribution of species. In the ecological field, the BioClimatic predictor (BioClim), which is most related to the physiological characteristics of organisms, is used for vulnerability assessment. However, BioClim values are not provided other than the future period climate average values for each GCM for the Shared Socio-economic Pathways (SSPs) scenario. In this study, BioClim data suitable for domestic conditions was produced using 1 km resolution SSPs scenario detailed data produced by Rural Development Administration, and based on the data, a species distribution model was applied to mainly grow in southern, Gyeongsangbuk-do, Gangwon-do and humid regions. Appropriate habitat distributions were predicted every 30 years for the base years (1981 - 2010) and future years (2011 - 2100) of the Acer pictum subsp. mono. Acer pictum subsp. mono appearance data were collected from a total of 819 points through the national natural environment survey data. In order to improve the performance of the MaxEnt model, the parameters of the model (LQH-1.5) were optimized, and 7 detailed biolicm indices and 5 topographical indices were applied to the MaxEnt model. Drainage, Annual Precipitation (Bio12), and Slope significantly contributed to the distribution of Acer pictum subsp. mono in Korea. As a result of reflecting the growth characteristics that favor moist and fertile soil, the influence of climatic factors was not significant. Accordingly, in the base year, the suitable habitat for a high level of Acer pictum subsp. mono is 3.41% of the area of Korea, and in the near future (2011 - 2040) and far future (2071 - 2100), SSP1-2.6 accounts for 0.01% and 0.02%, gradually decreasing. However, in SSP5-8.5, it was 0.01% and 0.72%, respectively, showing a tendency to decrease in the near future compared to the base year, but to gradually increase toward the far future. This study confirms the future distribution of vegetation that is more easily adapted to climate change, and has significance as a basic study that can be used for future forest restoration of climate change-adapted species.

• Journal of Wetlands Research
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• v.25 no.2
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• pp.145-158
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• 2023

Constructed wetlands (CWs) are effective technologies for urban wastewater management, utilizing natural physico-chemical and biological processes to remove pollutants. This study employed a bibliometric analysis approach to investigate the progress and future research trends in the field of CWs. A comprehensive review of 100 most-recently published and open-access articles was performed to analyze the performance of CWs in treating wastewater. Spain, China, Italy, and the United States were among the most productive countries in terms of the number of published papers. The most frequently used keywords in publications include water quality (n=19), phytoremediation (n=13), stormwater (n=11), and phosphorus (n=11), suggesting that the efficiency of CWs in improving water quality and removal of nutrients were widely investigated. Among the different types of CWs reviewed, hybrid CWs exhibited the highest removal efficiencies for BOD (88.67%) and TSS (95.67%), whereas VSSF, and HSSF systems also showed high TSS removal efficiencies (83.25%, and 78.83% respectively). VSSF wetland displayed the highest COD removal efficiency (71.82%). Generally, physical processes (e.g., sedimentation, filtration, adsorption) and biological mechanisms (i.e., biodegradation) contributed to the high removal efficiency of TSS, BOD, and COD in CW systems. The hybrid CW system demonstrated highest TN removal efficiency (60.78%) by integrating multiple treatment processes, including aerobic and anaerobic conditions, various vegetation types, and different media configurations, which enhanced microbial activity and allowed for comprehensive nitrogen compound removal. The FWS system showed the highest TP removal efficiency (54.50%) due to combined process of settling sediment-bound phosphorus and plant uptake. Phragmites, Cyperus, Iris, and Typha were commonly used in CWs due to their superior phytoremediation capabilities. The study emphasized the potential of CWs as sustainable alternatives for wastewater management, particularly in urban areas.

• Journal of the Korean Society of Radiology
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• v.17 no.4
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• pp.489-495
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• 2023

Radiation shielding facilities are constructed in locations where diagnostic radiation generators are installed, with the aim of preventing exposure for patients and radiation workers. The purpose of this study is seek to compare and validate the trend of attenuation thickness of lead, the primary material in these radiation shielding facilities, at different maximum tube voltages by Monte Carlo simulations and measurement. We employed the Monte Carlo N-Particle 6 simulation code. Within this simulation, we set a lead shielding arrangement, where the distance between the source and the lead sheet was set at 100 cm and the field of view was set at 10 × 10 cm². Additionally, we varied the tube voltages to encompass 80, 100, 120, and 140 kVp. We calculated energy spectra for each respective tube voltage and applied them in the simulations. Lead thicknesses corresponding to attenuation rates of 50, 70, 90, and 95% were determined for tube voltages of 80, 100, 120, and 140 kVp. For 80 kVp, the calculated thicknesses for these attenuation rates were 0.03, 0.08, 0.21, and 0.33 mm, respectively. For 100 kVp, the values were 0.05, 0.12, 0.30, and 0.50 mm. Similarly, for 120 kVp, they were 0.06, 0.14, 0.38, and 0.56 mm. Lastly, at 140 kVp, the corresponding thicknesses were 0.08, 0.16, 0.42, and 0.61 mm. Measurements were conducted to validate the calculated lead thicknesses. The radiation generator employed was the GE Healthcare Discovery XR 656, and the dosimeter used was the IBA MagicMax. The experimental results showed that at 80 kVp, the attenuation rates for different thicknesses were 43.56, 70.33, 89.85, and 93.05%, respectively. Similarly, at 100 kVp, the rates were 52.49, 72.26, 86.31, and 92.17%. For 120 kVp, the attenuation rates were 48.26, 71.18, 87.30, and 91.56%. Lastly, at 140 kVp, they were measured 50.45, 68.75, 89.95, and 91.65%. Upon comparing the simulation and experimental results, it was confirmed that the differences between the two values were within an average of approximately 3%. These research findings serve to validate the reliability of Monte Carlo simulations and could be employed as fundamental data for future radiation shielding facility construction.

• Journal of Korea Water Resources Association
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• v.56 no.2
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• pp.75-89
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• 2023

As the Mekong River basin is a nationally shared river, it is difficult to collect precipitation data, and the quantitative and qualitative quality of the data sets differs from country to country, which may increase the uncertainty of hydrological analysis results. Recently, with the development of remote sensing technology, it has become easier to obtain grid-based precipitation products(GPPs), and various hydrological analysis studies have been conducted in unmeasured or large watersheds using GPPs. In this study, rainfall-runoff simulation in the Mekong River basin was conducted using the SWAT model, which is a quasi-distribution model with three satellite GPPs (TRMM, GSMaP, PERSIANN-CDR) and two GPPs (APHRODITE, GPCC). Four water level stations, Luang Prabang, Pakse, Stung Treng, and Kratie, which are major outlets of the main Mekong River, were selected, and the parameters of the SWAT model were calibrated using APHRODITE as an observation value for the period from 2001 to 2011 and runoff simulations were verified for the period form 2012 to 2013. In addition, using the ConvAE, a convolutional neural network model, spatio-temporal correction of original satellite precipitation products was performed, and rainfall-runoff performances were compared before and after correction of satellite precipitation products. The original satellite precipitation products and GPCC showed a quantitatively under- or over-estimated or spatially very different pattern compared to APHPRODITE, whereas, in the case of satellite precipitation prodcuts corrected using ConvAE, spatial correlation was dramatically improved. In the case of runoff simulation, the runoff simulation results using the satellite precipitation products corrected by ConvAE for all the outlets have significantly improved accuracy than the runoff results using original satellite precipitation products. Therefore, the bias correction technique using the ConvAE technique presented in this study can be applied in various hydrological analysis for large watersheds where rain guage network is not dense.

• Journal of IKEEE
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• v.27 no.1
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• pp.116-121
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• 2023

In this paper, we develop a deep learning structure for a complex microbial incubator that applies deep learning prediction result information. The proposed complex microbial incubator consists of pre-processing of complex microbial data, conversion of complex microbial data structure, design of deep learning network, learning of the designed deep learning network, and GUI development applied to the prototype. In the complex microbial data preprocessing, one-hot encoding is performed on the amount of molasses, nutrients, plant extract, salt, etc. required for microbial culture, and the maximum-minimum normalization method for the pH concentration measured as a result of the culture and the number of microbial cells to preprocess the data. In the complex microbial data structure conversion, the preprocessed data is converted into a graph structure by connecting the water temperature and the number of microbial cells, and then expressed as an adjacency matrix and attribute information to be used as input data for a deep learning network. In deep learning network design, complex microbial data is learned by designing a graph convolutional network specialized for graph structures. The designed deep learning network uses a cosine loss function to proceed with learning in the direction of minimizing the error that occurs during learning. GUI development applied to the prototype shows the target pH concentration (3.8 or less) and the number of cells (10⁸ or more) of complex microorganisms in an order suitable for culturing according to the water temperature selected by the user. In order to evaluate the performance of the proposed microbial incubator, the results of experiments conducted by authorized testing institutes showed that the average pH was 3.7 and the number of cells of complex microorganisms was 1.7 × 10⁸. Therefore, the effectiveness of the deep learning structure for the complex microbial incubator applying the deep learning prediction result information proposed in this paper was proven.