• Journal of the Semiconductor & Display Technology
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• v.19 no.4
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• pp.88-93
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• 2020

Based on the capture-emission energy (CEE) maps of CMOS devices, a physics-informed machine learning model for the bias temperature instability (BTI)-induced threshold voltage shifts and low frequency noise is presented. In order to incorporate physics theories into the machine learning model, the integration of artificial neural network (IANN) is employed for the computation of the threshold voltage shifts and low frequency noise. The model combines the computational efficiency of IANN with the optimal estimation of Gaussian mixture model (GMM) with soft clustering. It enables full lifetime prediction of BTI under various stress and recovery conditions and provides accurate prediction of the dynamic behavior of the original measured data.

• Journal of Korean Society of Transportation
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• v.36 no.1
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• pp.1-12
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• 2018

The purpose of this study is to establish a methodology for evaluating quantitative effects of transportation GHG (greenhouse gas) reduction-related policies that were implemented based on the reduction goals of transportation GHG and effective implementation plans. This study uses a modal utility function and demand estimation models as well as a GHG emission basic unit estimation model by each transportation mode based on actual traffic and emission data. The results showed that the effects of GHG reduction policies such as electric vary from region to region, and from vehicle to vehicle. It is also confirmed that an eco-drive promotion policy, one of the lowest budget policies, is expected to contribute to high reduction in GHG. In addition, not only automobile emission improvement policies but also the promotion policies of public transportation are expected to highly reduce GHG as confirmed quantitatively in this study. The results of this study are expected to be useful for national and local governments' evaluation of GHG reduction policies to cope with the post 2020.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.6
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• pp.1-8
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• 2019

Carbon dioxide is one of the major driving forces causing climate changes, and many countries have been trying to reduce carbon dioxide emissions from various sources. Soil stores more carbon dioxide(two to three times) amounts than atmosphere indicating that soil organic carbon emission management are a pivotal issue. In this study, we developed a Soil Organic Carbon(SOC) storage estimation model to predict SOC storage amounts in soils. Also, SOC storage values were assessed based on the carbon emission price provided from Republic Of Korea(ROK). Here, the SOC model calculated the soil hydraulic properties based on the soil physical and chemical information. Base on the calculated the soil hydraulic properties and the soil physical chemical information, SOC storage amounts were estimated. In validation, the estimated SOC storage amounts were 486,696 tons($3.526kg/m^2$) in Jindo-gun and shown similarly compared to the previous literature review. These results supported the robustness of our SOC model in estimating SOC storage amounts. The total SOC storage amount in ROK was 305 Mt, and the SOC amount at Gyeongsangbuk-do were relatively higher than other regions. But the SOC storage amount(per unit) was highest in Jeju island indicating that volcanic ashes might influence on the relatively higher SOC amount. Based on these results, the SOC storage value was shown as 8.4 trillion won in ROK. Even though our SOC model was not fully validated due to lacks of measured SOC data, our approach can be useful for policy-makers in reducing soil organic carbon emission from soils against climate changes.

• Atmosphere
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• v.29 no.1
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• pp.1-12
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• 2019

For the allergy patient who needs to know the situation about the extent of pollen risk, the National Institute of Meteorological Sciences developed a pollen forecasting system based on the Community Multiscale Air Quality Modeling (CMAQ). In the old system, pollen emission from the oak was estimated just based on the airborne concentration and meteorology factors, resulted in high uncertainty. For improving the quality of current pollen forecasting system, therefore the estimation of pollen emission is now corrected based on the observation of pollen emission at the oak forest to better reflect the real emission pattern. In this study, the performance of the previous (NIMS2014) and current (NIMS2016) model system was compared using observed oak pollen concentration. Daily pollen concentrations and emissions were simulated in pollen season 2016 and accuracy of onset and end of pollen season were evaluated. In the NIMS2014 model, pollen season was longer than actual pollen season; The simulated pollen season started 6 days earlier and finished 13.25 days later than the actual pollen season. The NIMS2016 model, however, the simulated pollen season started only 1.83 days later, and finished 0.25 days later than the actual pollen season, showing the improvement to predict the temporal range of pollen events. Also, the NIMS2016 model shows better performance for the prediction of pollen concentration, while there is a still large uncertainty to capture the maximum pollen concentration at the target site. Continuous efforts to correct these problems will be required in the future.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.4
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• pp.36-45
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• 2014

Model based control methods are widely used to improve the control performance of diesel engine air systems because the control results of the air system significantly affect the emission level and drivability. However, the model based control algorithm requires a lot of unmeasurable states which are hard to be measured in a mass production engine. In this study, an air system model of the diesel engine is proposed to estimate 11 unmeasurable states using only sensors equipped in a mass production engine. In order to improve the estimation performance in the transient condition, dynamic characteristics of the air system are analyzed and implemented as discrete filters. Turbine and compressor efficiency models are also proposed to overcome a limitation of the constant or look-up table based efficiency values. The proposed air system model was validated in steady state and transient conditions by real-time engine experiments. The maximum error of the estimation for 11 physical states was 11.7%.

• Journal of Korean Society for Atmospheric Environment
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• v.25 no.6
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• pp.539-549
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• 2009

The purpose of this study is to estimate hourly Asian dust emission flux in springtime by using the optimized Weather Research Forecasting model (WRF) in order to accurately predict the horizontal flux of Asian dusts. Asian dust emission flux using 5 empirical formulas such as US EPA, Park and Inn, Wang, The Goddard Chemistry Aerosol Radiation and Transport (GOCART) and Dust Entrainment and Deposition (DEAD) were calculated and compared by using classified land-use types and size distribution at various locations in China and Mongolia together with the hourly meteorological elements of the WRF model. As a result, the empirical formula in US EPA among them, which was considered the various conditions such as vegetation, soil type and terrain, was better than the other 4 empirical formulas. However, these formulas were adjusted hourly and vertically in time and space because there was different order and time resolution of dust emissions from original empirical formulas.

• Advances in environmental research
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• v.9 no.2
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• pp.97-107
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• 2020

The indiscriminate growth in global population poses a threat to the world in handling and disposal of Municipal solid waste. Rapid urban growth increases the production, consumption and generation of Municipal solid waste which leads to a drastic change in the environment. The methane produced from the Municipal Solid waste accounts for up to 11% global anthropogenic emissions, which is a major cause for global warming. This study reports the methane emission estimation using IPCC default, TNO, LandGEM, EPER and close flux chamber from open dump yards at Perungudi and Kodungaiyur in Chennai, India. The result reveals that the methane emission using close flux chamber was in the range of 8.8 Gg/yr-11.3 Gg/yr and 6.1Gg/yr to 9.1 Gg/yr at Kodungaiyur and Perungudi dump yard respectively. The per capita waste generation was estimated based on waste generation and population. The waste generation potential was projected using linear regression model for the period 2017-2050. The trend of CH₄ emission in the actual field measurement were increased every year, similarly the emission trend also increased in IPCC default method (mass balance approach), EPER Germany (zero order decay model) where as TNO and Land GEM (first order decay model) were decreased. The present study reveals that Kodungaiyur dump yard is more vulnerable to methane emission compared to Perungudi dump yard and has more potential in waste to energy conversion mechanisms than compare to Perungudi dump yard.

• Journal of ILASS-Korea
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• v.25 no.4
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• pp.178-187
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• 2020

The current emission regulations, US Tier-4 and EU Stage-V, are only able to satisfy the regulations when all currently mass-produced emission reduction technologies such as EGR, DOC, DPF, and SCR are applied. Therefore, in this study, for the application of the Urea-SCR system to non-road diesel engines, the database was established by measuring the NO, NO2 concentration and calculating the NO2/NOx ratio based on the catalyst temperature and exhaust mass flow rate. Also, based on the measured NO2/NOx ratio data, a mathematical model was proposed to predict the NO2/NOx ratio at SCR catalyst, and the suitability of the model was verified through steady-state and transient mode. As a result of comparing the NO2/NOx ratio measured at the DOC outlet under the steady-state condition to two model values separately, the R2 was 0.9811 for the 3D map model and 0.9303 for the mathematical model. And in the case of the NO2/NOx ratio measured at the DPF outlet, the R2 was 0.9797 for the 3D map model and 0.935 for the mathematical model. It was confirmed that the R2 with the model value of the 3D Map of the mathematical model in the transient mode is 0.957, which shows high reliability.

• Journal of Korea Multimedia Society
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• v.16 no.12
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• pp.1465-1474
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• 2013

In this paper, air pollutant tracing models using perceptron neural network(PNN) and non-negative least square(NNLS) are proposed. When the measured values of the air pollution and the contribution concentration of each source by chemical transport modeling are given, they estimate and trace the amount of the air pollutants emission from each source. Two kinds of emissions data are used in the experiments : CH4 and N2O of Geumgo-dong landfill greenhouse gas, and PM10 of 17 areas in Northeast Asia and eight regions of the Korean Peninsula. Emission values were calculated using pseudo inverse method, PNN and NNLS. Pseudo inverse method could be used for the model, but it may have negative emission values. In order to deal with the problem, we used the PNN and NNLS methods. As a result, the estimation using the NNLS is closer to the measured values than that using PNN. The proposed tracing models have better utilization and generalization than those of conventional pseudo inverse model. It could be used more efficiently for air quality management and air pollution reduction.

• Journal of Environmental Policy
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• v.12 no.4
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• pp.45-69
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• 2013

This study estimates baseline greenhouse gas emissions from transport sector when a new town is developed. It has adopted a general greenhouse gas estimation model developed by Schipper, celine, Roger(2000) for the estimation, and showed how various transport related statistics can be utilized in detail. Particularly, it has produced unit greenhouse gas emission factor per vehicle types, vehicle-km, and trip-km. To evaluate effects of greenhouse gas reduction policies, it has calculated how much emissions will be reduced from bicycle promotion. It has turned out that about 369 thousand tons of carbon dioxide will be emitted from transport sector once the 1st Geomdan New Town is developed in Incheon metropolitan city. If the policy of bicycle promotion can attract people to use bicycle as much as 5% of total trips, then it can reduce about 1,869 tons of carbon dioxide.