• Applied Chemistry for Engineering
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• v.32 no.3
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• pp.305-311
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• 2021

Effects of chitosan as a mesopore directing agent of SAPO-34 catalysts were investigated to improve the catalytic lifetime in DTO reaction. The synthesized catalysts were characterized by XRD, SEM, N₂ adsorption-desorption isotherm and NH₃-temperature programmed desorption (TPD). The modified SAPO-34 catalysts prepared by varying the added amount of chitosan showed the same cubic morphology and chabazite structure as the conventional SAPO-34 catalyst. As the added amount of chitosan increased to 3 wt%, the surface area, mesopore volume and concentration of weak acid sites of modified SAPO-34 catalysts increased. The modified SAPO-34 catalysts showed enhanced catalytic lifetime and high selectivity for light olefins in the DTO reaction. In particular, the SAPO-CHI 3 catalyst (3 wt%) exhibited the longest catalytic lifetime than that of the conventional SAPO-34. Therefore, it was confirmed that chitosan was a suitable material as a mesopore directing agent to delay deactivation of the SAPO-34 catalyst.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.10
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• pp.1331-1340
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• 2020

Object detection in maritime means that the captain detects floating objects that has a risk of colliding with the ship using the computer automatically and as accurately as human eyes. In conventional ships, the presence and distance of objects are determined through radar waves. However, it cannot identify the shape and type. In contrast, with the development of AI, cameras help accurately identify obstacles on the sea route with excellent performance in detecting or recognizing objects. The computer must calculate high-volume pixels to analyze digital images. However, the CPU is specialized for sequential processing; the processing speed is very slow, and smooth service support or security is not guaranteed. Accordingly, this study developed maritime object detection software and implemented it with FPGA to accelerate the processing of large-scale computations. Additionally, the system implementation was improved through embedded boards and FPGA interface, achieving 30 times faster performance than the existing algorithm and a three-times faster entire system.

• Journal of Korean Society of Forest Science
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• v.110 no.1
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• pp.64-71
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• 2021

Recently, climate change has gradually accelerated the occurrence of landslides. Among the various effects caused by landslides,debris flow is recognized as particularly threatening because of its high speed and propagating distance. In this study, the impacts of various factors were analyzed using quantification theory(I) for the prediction of debris flow hazard soil volume in Seoraksan National Park, Korea. According to the range using the stepwise regression analysis, the order of impact factors was as follows: vertical slope (0.9676), cross slope (0.6876), altitude (0.2356), slope gradient (0.1590), and aspect (0.1364). The extent of the normalized score using the five-factor categories was 0 to 2.1864, with the median score being 1.0932. The prediction criteria for debris flow occurrence based on the normalized score were divided into four grades: class I, >1.6399; class II, 1.0932-1.6398; class III, 0.5466-1.0931; and class IV, <0.5465. Predictions of debris flow occurrence appeared to be relatively accurate (86.3%) for classes I and II. Therefore, the prediction criteria for debris flow will be useful for judging the dangerousness of slopes.

• Journal of Food Hygiene and Safety
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• v.35 no.6
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• pp.587-601
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• 2020

Exposure to hazardous substances occurs through multiple pathways. Aggregated risk assessment, which includes all potential exposure pathways to a single toxicant, is necessary to prevent exposure to harmful substances. We aimed to estimate cadmium and lead exposure through various media, such as food, water, air, smoking, cosmetics, and female hygiene products. This study covered 10,733 subjects from the Seventh Korea National Health and Nutrition Examination Survey(2016, 2017). Dietary exposure was estimated using 24-hour recall data. For water and inhalational exposure, regional variations were considered. Water was classified as tap, bottled, and public water. Inhalational exposure was estimated using the '2014 Time Use Survey' based on daily lifestyle and social status. The frequency and volume of cosmetic usage were randomly approximated by sex and age. Post-menarcheal and premenopausal women were assumed to use feminine hygiene products. Non-carcinogenic aggregated risks were estimated using the Aggregate Risk Index from EPAs and the Total Exposure Hazard Index from Korean government guidelines. For carcinogenic risk assessment, excessive cancer risk was estimated. Ingestion, especially food, was the major route for both cadmium and lead exposure. Smoking was also associated with high cadmium exposure. Exposure to lead from cosmetics was remarkable but not critical. In aggregate risk assessments, median cadmium and lead exposure did not exceed the reference value. Sex, age, smoking status, and income affected exposure levels, unlike to regional variations.

• Journal of Advanced Navigation Technology
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• v.25 no.1
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• pp.1-7
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• 2021

Detect and avoid (DAA) system, which is essential for the operation of UAS, detects intruding aircraft and offers the ranges of turn and climb/descent maneuver that are required to avoid the intruder. This paper uses detect and avoid alerting logic for unmanned systems (DAIDALUS) developed at NASA as a DAA algorithm. Since DAIDALUS offers ranges of avoidance maneuvers, the actual avoidance maneuver must be decided by the UAS pilot as well as the timing and method of returning to the original route. It can be readily used in real-time human-in-the-loop (HiTL) simulations where a human pilot is making the decision, but a pilot decision model is required in fast-time simulations that proceed without human pilot intervention. This paper proposes a pilot decision model that maneuvers the aircraft based on the DAIDALUS avoidance maneuver range. A series of tests were conducted using test vectors from radio technical commission for aeronautics (RTCA) minimum operational performance standards (MOPS). The alert levels differed by the types of encounters, but loss of well clear (LoWC) was avoided. This model will be useful in fast-time simulation of high-volume traffic involving UAS.

• Journal of Biomedical Engineering Research
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• v.42 no.4
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• pp.150-158
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• 2021

Intracranial hemorrhage (ICH) refers to acute bleeding inside the intracranial vault. Not only does this devastating disease record a very high mortality rate, but it can also cause serious chronic impairment of sensory, motor, and cognitive functions. Therefore, a prompt and professional diagnosis of the disease is highly critical. Noninvasive brain imaging data are essential for clinicians to efficiently diagnose the locus of brain lesion, volume of bleeding, and subsequent cortical damage, and to take clinical interventions. In particular, computed tomography (CT) images are used most often for the diagnosis of ICH. In order to diagnose ICH through CT images, not only medical specialists with a sufficient number of diagnosis experiences are required, but even when this condition is met, there are many cases where bleeding cannot be successfully detected due to factors such as low signal ratio and artifacts of the image itself. In addition, discrepancies between interpretations or even misinterpretations might exist causing critical clinical consequences. To resolve these clinical problems, we developed a diagnostic model predicting intracranial bleeding and its subtypes (intraparenchymal, intraventricular, subarachnoid, subdural, and epidural) by applying deep learning algorithms to CT images. We also constructed a visualization tool highlighting important regions in a CT image for predicting ICH. Specifically, 1) 27,758 CT brain images from RSNA were pre-processed to minimize the computational load. 2) Three different CNN-based models (ResNet, EfficientNet-B2, and EfficientNet-B7) were trained based on a training image data set. 3) Diagnosis performance of each of the three models was evaluated based on an independent test image data set: As a result of the model comparison, EfficientNet-B7's performance (classification accuracy = 91%) was a way greater than the other models. 4) Finally, based on the result of EfficientNet-B7, we visualized the lesions of internal bleeding using the Grad-CAM. Our research suggests that artificial intelligence-based diagnostic systems can help diagnose and treat brain diseases resolving various problems in clinical situations.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.6
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• pp.409-415
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• 2021

Medical waste management is becoming increasingly important, specifically in light of the current COVID-19 pandemic, as hospitals, clinics, quarantine centers, and medical research institutes are generating tons of medical waste every day. Previously, a traditional incineration process was utilized for managing medical waste, but the lack of landfill sites, and accompanying environmental concerns endanger public health. Consequently, an innovative sterilization shredding system was developed to resolve this problem. In this research, we focused on the design and numerical analysis of a shredding system for hazardous and infectious medical waste, to establish its operational performance. The shredding machine's components were modeled in a CAD application, and finite element analysis (FEA) was conducted using ABAQUS software. Static, fatigue, and dynamic loading conditions were used to analyze the structural stability of the cutting blade. The blade geometry proved to be effective based on the cutting force applied to shred medical waste. The dynamic stability of the structure was verified using modal analysis. Furthermore, an S-N curve was generated using a high cycle fatigue study, to predict the expected life of the cutting blade. Resultantly, an appropriate shredder system was devised to link with a sterilization unit, which could be beneficial in reducing the volume of medical waste and disposal time, thereof, thus eliminating environmental issues, and potential health hazards.

• Tribology and Lubricants
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• v.37 no.6
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• pp.232-239
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• 2021

Petroleum is the most consumed energy source in Korea with a usage rate of 38.7% among the available primary energy sources. The price of liquid petroleum products in Korea includes taxes such as transportation·environment·energy tax. Thus, illegal production and distribution of liquid petroleum is widespread because of its huge price difference from that of the normal product and its tax-free nature. Generally, the illegal petroleum product is produced by mixing liquid petroleum with other similar petroleum alternatives. The two kinds of gasoline, common gasoline and premium gasoline, are being distributed in Korea. The premium gasoline is often adulterated with cheaper common gasoline that lowers the octane number of gasoline. It is possible to distinguish them with their color difference, green and yellow for different grade gasoline. However, when small volume of common gasoline is added to premium gasoline, it is difficult to determine whether premium gasoline contained common grade or not. In this study, we inspect gasoline, which is illegally produced by mixing common gasoline to premium gasoline. When the ratio of mixing common gasoline is increased, premium gasoline shows decreasing absorbance at 600 nm and 650 nm under UV-Vis spectrometer. Moreover, the detected intensity (mV·s) of green dye in high performance liquid chromatography (HPLC) was decreased by common gasoline under 0.99 correlation value. The more the common gasoline is mixed, the more olefin and naphthene are detected by gas chromatography. In addition, trimethyl pentane as octane improver, paraffin and toluene are decreased by common gasoline mixing. The findings of this study suggests that illegal petroleum can be identified by analysis of components and simulated samples.

• Journal of the Korea Organic Resources Recycling Association
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• v.29 no.4
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• pp.87-97
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• 2021

This study was conducted to prepare an incentive system (proposal) for the activation of waste-to-energy. Weights for each type of energy use were prepared by conducting prior research and economic analysis. In addition, the waste-to-energy incentive (proposal) was calculated in consideration of energy efficiency for each type of energy use. As a result of economic analysis of 11 biogasification facilities, the B/C value was found to be very diverse, ranging from 0.16 to 1.69. In terms of benefits, imports of waste treatment import fees were very high at 68.4 to 99.3% of the total, and four facilities with a surplus (+) or higher in the management balance. In order to convert energy consumption into units of sales volume, 0.58 Nm³/KW for power generation, 0.17 Nm³/kg for steam, and 1.00 Nm³/Nm³ for external supply were calculated using the 'scale factor'. The 'weight factor' was calculated as 0.249 for power generation, 0.656 for steam, and 0.806 for external supply, respectively, by use type.

• Environmental and Resource Economics Review
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• v.30 no.4
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• pp.627-648
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• 2021

The fine-dust season management system refers to the policy of implementing enhanced reduction measures in transportation, power, business and living sectors in winter, when fine dust levels are high. The fine dust season management system is a regulatory policy that causes social costs and transfers to various economic players. Equity is an important issue for the cost burden. Therefore, in this study, the cost of each power generator was analyzed using the coal power generation reduction amount of each power generator to verify that the cost of the power sector is evenly distributed. In particular, the effect of the fine dust season management system on coal power generation of power generators was analyzed by applying a synthetic control method that can identify the time-variable effect of the policy. It was confirmed that the fine dust season management system reduced volume of fuel and power generation in coal power plants, resulting in an increase in the cost of the power generation sector, even considering the effect of some power demand due to the COVID-19 crisis. However, it could be seen that these costs were not distributed equally among the generators, and that they were more costly to the specific generators.Social costs incurred by fine dust season management need to be improved so that stakeholders are equally burdened.