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A study on the air pollutant emission trends in Gwangju (광주시 대기오염물질 배출량 변화추이에 관한 연구)

  • Seo, Gwang-Yeob;Shin, Dae-Yewn
    • Journal of environmental and Sanitary engineering
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    • v.24 no.4
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    • pp.1-26
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    • 2009
  • We conclude the following with air pollution data measured from city measurement net administered and managed in Gwangju for the last 7 years from January in 2001 to December in 2007. In addition, some major statistics governed by Gwangju city and data administered by Gwangju as national official statistics obtained by estimating the amount of national air pollutant emission from National Institute of Environmental Research were used. The results are as follows ; 1. The distribution by main managements of air emission factory is the following ; Gwangju City Hall(67.8%) > Gwangsan District Office(13.6%) > Buk District Office(9.8%) > Seo District Office(5.5%) > Nam District Office(3.0%) > Dong District Office(0.3%) and the distribution by districts of air emission factory ; Buk District(32.8%) > Gwangsan District(22.4%) > Seo District(21.8%) > Nam District(14.9%) > Dong District(8.1%). That by types(Year 2004~2007 average) is also following ; Type 5(45.2%) > Type 4(40.7%) > Type 3(8.6%) > Type 2(3.2%) > Type 1(2.2%) and the most of them are small size of factory, Type 4 and 5. 2. The distribution by districts of the number of car registrations is the following ; Buk District(32.8%) > Gwangsan District(22.4%) > Seo District(21.8%) > Nam District(14.9%) > Dong District(8.1%) and the distribution by use of car fuel in 2001 ; Gasoline(56.3%) > Diesel(30.3%) > LPG(13.4%) > etc.(0.2%). In 2007, there was no ranking change ; Gasoline(47.8%) > Diesel(35.6%) > LPG(16.2%) >etc.(0.4%). The number of gasoline cars increased slightly, but that of diesel and LPG cars increased remarkably. 3. The distribution by items of the amount of air pollutant emission in Gwangju is the following; CO(36.7%) > NOx(32.7%) > VOC(26.7%) > SOx(2.3%) > PM-10(1.5%). The amount of CO and NOx, which are generally generated from cars, is very large percentage among them. 4. The distribution by mean of air pollutant emission(SOx, NOx, CO, VOC, PM-10) of each county for 5 years(2001~2005) is the following ; Buk District(31.0%) > Gwangsan District(28.2%) > Seo District(20.4%) > Nam District(12.5%) > Dong District(7.9%). The amount of air pollutant emission in Buk District, which has the most population, car registrations, and air pollutant emission businesses, was the highest. On the other hand, that of air pollutant emission in Dong District, which has the least population, car registrations, and air pollutant emission businesses, was the least. 5. The average rates of SOx for 5 years(2001~2005) in Gwangju is the following ; Non industrial combustion(59.5%) > Combustion in manufacturing industry(20.4%) > Road transportation(11.4%) > Non-road transportation(3.8%) > Waste disposal(3.7%) > Production process(1.1%). And the distribution of average amount of SOx emission of each county is shown as Gwangsan District(33.3%) > Buk District(28.0%) > Seo District(19.3%) > Nam District(10.2%) > Dong District(9.1%). 6. The distribution of the amount of NOx emission in Gwangju is shown as Road transportation(59.1%) > Non-road transportation(18.9%) > Non industrial combustion(13.3%) > Combustion in manufacturing industry(6.9%) > Waste disposal(1.6%) > Production process(0.1%). And the distribution of the amount of NOx emission from each county is the following ; Buk District(30.7%) > Gwangsan District(28.8%) > Seo District(20.5%) > Nam District(12.2%) > Dong District(7.8%). 7. The distribution of the amount of carbon monoxide emission in Gwangju is shown as Road transportation(82.0%) > Non industrial combustion(10.6%) > Non-road transportation(5.4%) > Combustion in manufacturing industry(1.7%) > Waste disposal(0.3%). And the distribution of the amount of carbon monoxide emission from each county is the following ; Buk District(33.0%) > Seo District(22.3%) > Gwangsan District(21.3%) > Nam District(14.3%) > Dong District(9.1%). 8. The distribution of the amount of Volatile Organic Compound emission in Gwangju is shown as Solvent utilization(69.5%) > Road transportation(19.8%) > Energy storage & transport(4.4%) > Non-road transportation(2.8%) > Waste disposal(2.4%) > Non industrial combustion(0.5%) > Production process(0.4%) > Combustion in manufacturing industry(0.3%). And the distribution of the amount of Volatile Organic Compound emission from each county is the following ; Gwangsan District(36.8%) > Buk District(28.7%) > Seo District(17.8%) > Nam District(10.4%) > Dong District(6.3%). 9. The distribution of the amount of minute dust emission in Gwangju is shown as Road transportation(76.7%) > Non-road transportation(16.3%) > Non industrial combustion(6.1%) > Combustion in manufacturing industry(0.7%) > Waste disposal(0.2%) > Production process(0.1%). And the distribution of the amount of minute dust emission from each county is the following ; Buk District(32.8%) > Gwangsan District(26.0%) > Seo District(19.5%) > Nam District(13.2%) > Dong District(8.5%). 10. According to the major source of emission of each items, that of oxides of sulfur is Non industrial combustion, heating of residence, business and agriculture and stockbreeding. And that of NOx, carbon monoxide, minute dust is Road transportation, emission of cars and two-wheeled vehicles. Also, that of VOC is Solvent utilization emission facilities due to Solvent utilization. 11. The concentration of sulfurous acid gas has been 0.004ppm since 2001 and there has not been no concentration change year by year. It is considered that the use of sulfurous acid gas is now reaching to the stabilization stage. This is found by the facts that the use of fuel is steadily changing from solid or liquid fuel to low sulfur liquid fuel containing very little amount of sulfur element or gas, so that nearly no change in concentration has been shown regularly. 12. Concerning changes of the concentration of throughout time, the concentration of NO has been shown relatively higher than that of $NO_2$ between 6AM~1PM and the concentration of $NO_2$ higher during the other time. The concentration of NOx(NO, $NO_2$) has been relatively high during weekday evenings. This result shows that there is correlation between the concentration of NOx and car traffics as we can see the Road transportation which accounts for 59.1% among the amount of NOx emission. 13. 49.1~61.2% of PM-10 shows PM-2.5 concerning the relationship between PM-10 and PM-2.5 and PM-2.5 among dust accounts for 45.4%~44.5% of PM-10 during March and April which is the lowest rates. This proves that particles of yellow sand that are bigger than the size $2.5\;{\mu}m$ are sent more than those that are smaller from China. This result shows that particles smaller than $2.5\;{\mu}m$ among dust exist much during July~August and December~January and 76.7% of minute dust is proved to be road transportation in Gwangju.

Performance Evaluation of Siemens CTI ECAT EXACT 47 Scanner Using NEMA NU2-2001 (NEMA NU2-2001을 이용한 Siemens CTI ECAT EXACT 47 스캐너의 표준 성능 평가)

  • Kim, Jin-Su;Lee, Jae-Sung;Lee, Dong-Soo;Chung, June-Key;Lee, Myung-Chul
    • The Korean Journal of Nuclear Medicine
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    • v.38 no.3
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    • pp.259-267
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    • 2004
  • Purpose: NEMA NU2-2001 was proposed as a new standard for performance evaluation of whole body PET scanners. in this study, system performance of Siemens CTI ECAT EXACT 47 PET scanner including spatial resolution, sensitivity, scatter fraction, and count rate performance in 2D and 3D mode was evaluated using this new standard method. Methods: ECAT EXACT 47 is a BGO crystal based PET scanner and covers an axial field of view (FOV) of 16.2 cm. Retractable septa allow 2D and 3D data acquisition. All the PET data were acquired according to the NEMA NU2-2001 protocols (coincidence window: 12 ns, energy window: $250{\sim}650$ keV). For the spatial resolution measurement, F-18 point source was placed at the center of the axial FOV((a) x=0, and y=1, (b)x=0, and y=10, (c)x=70, and y=0cm) and a position one fourth of the axial FOV from the center ((a) x=0, and y=1, (b)x=0, and y=10, (c)x=10, and y=0cm). In this case, x and y are transaxial horizontal and vertical, and z is the scanner's axial direction. Images were reconstructed using FBP with ramp filter without any post processing. To measure the system sensitivity, NEMA sensitivity phantom filled with F-18 solution and surrounded by $1{\sim}5$ aluminum sleeves were scanned at the center of transaxial FOV and 10 cm offset from the center. Attenuation free values of sensitivity wire estimated by extrapolating data to the zero wall thickness. NEMA scatter phantom with length of 70 cm was filled with F-18 or C-11solution (2D: 2,900 MBq, 3D: 407 MBq), and coincidence count rates wire measured for 7 half-lives to obtain noise equivalent count rate (MECR) and scatter fraction. We confirmed that dead time loss of the last flame were below 1%. Scatter fraction was estimated by averaging the true to background (staffer+random) ratios of last 3 frames in which the fractions of random rate art negligibly small. Results: Axial and transverse resolutions at 1cm offset from the center were 0.62 and 0.66 cm (FBP in 2D and 3D), and 0.67 and 0.69 cm (FBP in 2D and 3D). Axial, transverse radial, and transverse tangential resolutions at 10cm offset from the center were 0.72 and 0.68 cm (FBP in 2D and 3D), 0.63 and 0.66 cm (FBP in 2D and 3D), and 0.72 and 0.66 cm (FBP in 2D and 3D). Sensitivity values were 708.6 (2D), 2931.3 (3D) counts/sec/MBq at the center and 728.7 (2D, 3398.2 (3D) counts/sec/MBq at 10 cm offset from the center. Scatter fractions were 0.19 (2D) and 0.49 (3D). Peak true count rate and NECR were 64.0 kcps at 40.1 kBq/mL and 49.6 kcps at 40.1 kBq/mL in 2D and 53.7 kcps at 4.76 kBq/mL and 26.4 kcps at 4.47 kBq/mL in 3D. Conclusion: Information about the performance of CTI ECAT EXACT 47 PET scanner reported in this study will be useful for the quantitative analysis of data and determination of optimal image acquisition protocols using this widely used scanner for clinical and research purposes.

A Study of Perspective on Cheon Gwan(天觀) of Toegye (퇴계(退溪)의 천관(天觀) 연구(硏究))

  • Hwang, Sang Hee
    • (The)Study of the Eastern Classic
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    • no.56
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    • pp.147-170
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    • 2014
  • To divide by the concept of Cheon (天) before and after the period of Song Dynasty: before Song Dynasty; according to the ancient Book of Odes (Sigyeong-詩經), "Cheon (天) gives birth to a large number of people", and, Confucius(孔子) say "Cheon(天) gave me Virtue(德)." Mencius(孟子) say "The person done with all his heart knows Seong(性, personality), so if he knows such Seong(性, personality), then he knows Cheon(天)." In Doctrine of the Mean(中庸), it says "Cheon(天) ordered it to be called - Seong(性, personality)." So, Cheon(天) had a religious meaning, such as Sangje(上帝) - Supreme Ruler. During the Song period, Cheon(天), the source of its existence, had construed as Mugeuk i Taegeuk Non(無極而太極論 - Theory of Supreme Ultimate while being Indeterminate) and Theory of li and ki (iginon-理氣論). Juja (朱子, a honorary name of Juhui, 朱熹) had said a reasonable Cheon(天), that is, Heavenly Principle (天理 - Cheolli) by interpreting Cheon(天) as Taegeuk(太極 - Supreme Polarity) and li(理) of Muwi(無爲 - uncontrived action). That's why Juja had lost the religiosity because of his reasonable frame. The purpose of this dissertation is to identify of the quality of being religious of li(理) on the basis of attribute of Cheon(天) argued by Toegye and Juja. In the text of Seomyeong(西銘 - Western Inscription), we can see their interpretation of the content that Toegye as "西銘考證講義"(Lecture on Historical Research of Western Inscription), and Juja as "西銘解"(Commentary on the Western Inscription). Seomyeong(西銘 - Western Inscription) was expounded as a logic of 'iil bunsu' (理一分殊 - coherence is one and distinguished into many). '理一分殊' means to live in as meaningful as possible according to the human nature that has been bestowed upon thyself. Juja and Toegye both said that in the aspect of 'iil'(理一 - coherence is one), Reverence(事天) ought to be done, but to look into the aspect of 'bunsu'(分殊-distinguished into many), Juja argued that people should follow the order of Heavenly Principle(天理 - Cheolli), and Toegye argued that people should have to perform the filial piety(孝). There are differences in methods of Toegye and Juja on account of distinction between attributes of Cheon(天). Such a distinction affects the attribute of li(理). Juja said divisively that Soiyeon(所以然-why its principle is so) is li(理), and Sodangyeon(所當然-what should be so) is Sa(事-divine project). Toegye argued that Sodangyeon(所當然-what should be so) is indeed li(理). It is the position of Toegye that to know Seong(性-the personality) of Sodangyeon(所當然-what should be so) is the first, rather than to know Cheon(天) of Soiyeon(所以然-why its principle is so) that is out of reach in a faraway place. Seong(性-the personality) is li(理) that bestowed by Cheon(天). In view of discussion about the essence and existence, for Toegye, the existence is the first, rather than the essence. The issues of existence is now enabled to talk about amid the discussion of metaphysics, namely li(理). Different from Juja, a theory noticed in Toegye is the theory of 'Lijado'(理自到). 'Lijado'(理自到) denotes 'Li(理) leads on their own.' It tells that separate from thing-in-itself, there is an energy that moves and oversees the thing. This is an issue of response between "I" as the principal agent and other people. If "I" as the principal agent is sincere to others, the others will come to me insomuch as they will be revealed through me. Here, a problem between the host and guest arises. Toegye perceived this problem that do not see me and others as same, and also do not see me and others as two. This is the logic of 'ilii iiil'(一而二 二而一 - looks like one but two, looks like two but one) of '理一分殊' (coherence is one and distinguished into many). The first thing to do between these two processes is to recognize the existence of 'iil'(理一). Toegye strongly displays a religious attitude identifying Cheon(天)=Li (理)=Sangje(上帝- Supreme Ruler) in the same light.

Basic Research on the Possibility of Developing a Landscape Perceptual Response Prediction Model Using Artificial Intelligence - Focusing on Machine Learning Techniques - (인공지능을 활용한 경관 지각반응 예측모델 개발 가능성 기초연구 - 머신러닝 기법을 중심으로 -)

  • Kim, Jin-Pyo;Suh, Joo-Hwan
    • Journal of the Korean Institute of Landscape Architecture
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    • v.51 no.3
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    • pp.70-82
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    • 2023
  • The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.