• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.6
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• pp.123-134
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• 2013

Strengthening vehicle emission regulation is one of important policies to improve air quality in urban area. Due to the limitation of specified driving cycles for certification test to reflect real driving conditions, additional off-cycle emission regulations have been adopted in US and being developed in Europe. The driving cycles of US or Europe have been used in emission certification for Korean light-duty vehicles, but it has not been known how well the driving cycles reflect various real driving patterns in Korea. In that point of view, it is required to estimate vehicle emission based on real road driving conditions to raise the effectiveness of vehicle emission regulation in Korea. In this study, real driving emission measurements have been conducted for three Korean light-duty vehicles with PEMS. The driving routes consisted of urban, rural and motorway in Seoul and Incheon. The data have been analyzed with various averaging methods including moving averaging windows method and compared to emission limits set with emission certification modes applied to tested vehicles. The results have shown that the real driving pollutant emissions of a gasoline and a LPG vehicles have been ranged quite lower than those of emission limits on CVS-75 driving cycle. But real driving NOx of a light duty diesel vehicle has been considerably higher than emission limit of NEDC driving cycle. The higher than expected NOx emission of a diesel vehicle might be caused by different strategy to control EGR in real driving condition from NEDC driving.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.6
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• pp.1477-1484
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• 2015

This paper proposes a robust method for object tracking based on Kalman filters algorithm and covariance matrix. As a feature of the object to be tracked, covariance matrix ensures the continuity of the moving target tracking in the image frames because the covariance is addressed spatial and statistical properties as well as the correlation properties of the features, despite the changes of the form and shape of the target. However, if object moves faster than operation time, real time tracking is difficult. In order to solve the problem, Kalman filters are used to estimate the area of the moving object and covariance matrices as a feature vector are compared with candidate regions within the estimated Kalman window. The results show that the tracking rate of 96.3% achieved using the proposed method.

• Annual Conference of KIPS
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• 2018.10a
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• pp.362-364
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• 2018

Predicting network traffic volume has become a popular topic recently due to its support in many situations such as detecting abnormal network activities and provisioning network services. Especially, predicting the volume of the next upcoming traffic from the series of observed recent traffic volume is an interesting and challenging problem. In past, various techniques are researched by using time series forecasting methods such as moving averaging and exponential smoothing. In this paper, we propose a long short-term memory neural network (LSTM) based network traffic volume prediction method. The proposed method employs the changing rate of observed traffic volume, the corresponding time window index, and a seasonality factor indicating the changing trend as input features, and predicts the upcoming network traffic. The experiment results with real datasets proves that our proposed method works better than other time series forecasting methods in predicting upcoming network traffic.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.39 no.12
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• pp.965-973
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• 2015

Recently, an emission test procedure using a portable emissions measurement system(PEMS) has received much attention as an effective means of controlling real driving emissions from light-duty diesel vehicles. The PEMS-based test procedure will be implemented from 2017 in Europe and Korea as a complementary test procedure for certification and regulation. In the present study, on-road NOx emissions were measured for four kinds of Euro 5 Korean light-duty diesel vehicles under real driving conditions, including urban, rural, and motorway test routes. The real driving emission characteristics were evaluated using both a moving averaging window(MAW) and the weighted emission method(WEM). The evaluated NOx emission results (under real driving conditions) from the MAW and WEM showed similar tendencies for the test vehicles and routes, while exceeding the certification emission limit by 1.8~8.5 and 2.0~10.6 times, respectively.

• Journal of ILASS-Korea
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• v.21 no.4
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• pp.200-206
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• 2016

Recently, the certification procedure for exhaust emission regulation of LDV has tested with the NEDC mode in the laboratory. But the on-road exhaust emissions exceed the standard emission limits. Therefore, it is important to analyze the real-driving emissions (RDE) with a portable emissions measurement system (PEMS). In present study, the on-road emissions were measured with a PEMS and evaluated by moving averaging window (MAW) method. Also, it was compared with the $CO_2$ and $NO_x$ emissions for real-driving and test modes from euro-6 light-duty vehicles equipped with SCR and LNT systems. In results, on-road $NO_x$ emission has been 2.3-10.0 times higher than the standard $NO_x$ emission limit on NEDC mode. The reason was that the test modes did not reflect traffic and various real-driving patterns sufficiently.

• Journal of ILASS-Korea
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• v.21 no.4
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• pp.207-213
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• 2016

To protect air pollution of urban area from motor vehicles, emission limits for diesel vehicles have been dramatically lowered in short period. But recent studies have shown that on-road NOx emissions of light-duty diesel vehicles are considerably higher than the values measured with laboratory test procedures used for emission certification. To tackle with this issue, Ministry of Environment have a plan to introduce EU RDE-LDV (Real-driving Emission-Light-duty Vehicle) regulation. In this study, 4 Euro 6 diesel vehicles have been tested with the new test procedures published by EU to estimate on-road NOx emissions using PEMS (Portable Emission Measurement System). The results have shown that the requirements of EU RDE-LDV could be met in driving condition of metropolitan area for constitution of test routes and validity of test results. In analysing with Moving Averaging Window method the completeness and normality of test data were validated with the requirement. On-road NOx emissions were quite deviated as test vehicles and higher than the new limit of on-road NOx emission enforced from Sept. 2017, which means that RDE-LDV can effectively reduce NOx emission of diesel vehicles in real driving conditions of Korea.

• Journal of Climate Change Research
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• v.4 no.4
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• pp.359-369
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• 2013

In this paper, correlations between driving parameters and $CO_2$ of light-duty vehicles have been analyzed. Three test vehicles equipped with PEMS (Portable Emission Measurement System) have been driven in real-road in urban areas of Seoul. Averaged vehicle speed, RPA(Relative Positive Acceleration) and stop ratio have been selected as main driving parameters. The analysis have been conducted in interrupted and uninterrupted road types. Averaged values in various driving conditions have been calculated with distance based moving averaging window method. The multiple linear regression method have been applied to account for correlation between driving parameters and $CO_2$ emissions. This approach has shown statistically that $CO_2$ emission per distance (g/km) have tendencies to be increased as decreased averaged vehicle speed and increased RPA and stop ratio. Compared with uninterrupted traffic, interrupted traffic have shown the lower vehicle speed and the higher RPA and stop ratio. These characteristics of driving parameters in interrupted traffic should cause the higher $CO_2$ emission per distance.