• 한국ITS학회:학술대회논문집
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• 2008.11a
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• pp.480-487
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• 2008

• Journal of Korean Society of Transportation
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• v.27 no.5
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• pp.125-134
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• 2009

Recently, as IT technology and the ubiquitous environment have diffused, the application of these techniques are being attempted in the field of traffic operations and management. Therefore, it is necessary to develop data collection systems and signal control strategies that are suitable in the ubiquitous environment and that will improve efficiency and safety of signalized intersections. The authors conducted a study on the Wireless Sensor Network (WSN) signal control strategy using a wireless communication network between individual vehicles and a signal-control system and full actuated signal control technique to propose a new signal control strategy in the ubiquitous environment. The WSN was defined to evaluate the algorithm used with PARAMICS API simulation. The simulation produced results that the WSN signal control is more effective than other signal control methods. The WSN signal control could reduce vehicle delay time to a maximum of 64% in comparison with other signal control methods in low and near saturation flow conditions.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.1 no.1
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• pp.58-69
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• 2002

Most of the traffic accidents are a rear-end collision and a clash generated in the signalized intersection on the local roads. So, it is demanded that the high-quality of signal control and dilemma zone control. According to the cases generated by foreign countries, we established the strategies which are composed of Volume-Density Control, strategy of the dilemma zone control using R-detector (microwave detector) In Japan and EC-DC Control. MOEs(Measure of effectiveness) are car numbers in the dilemma zone , max-out probability in the safe side and the average stopping delay in the progress side. We choose a signalized intersection in rural highway to analyze the effect of the strategies and practiced an on-the-spot survey. The result of the survey is applied to the basic data in the simulator. Consequently, strategy of the dilemma zone control using R-detector(microwave detector) in Japan is the best effective in the safe side and EC-DC control is the best in the progress side. Based on the result, we developed the effective strategy of the signal control . This strategy is composed of the strategy of Japan and the detector on the stopping line used in the EC-DC control. On the result of the analysis, new strategy is the best effective in two sides.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.3
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• pp.617-622
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• 2018

Simultaneous control of blowers and heat exchangers affecting the air sealing and flow conditions inside the green house is essential to the management of $CO_2$ concentration. Currently, the demand for automation systems and integrated control is steadily increasing according to increasing of farm areas per person due to the reduction of agricultural population. This paper proposes the integrated control system that can control simultaneously the existing devices such as measurement switching devices which are important variables in the supply of $CO_2$, $CO_2$ generator fuel and combustion air mixture ratio(air-to-fuel ratio), and $CO_2$ supply control under internal and external conditions.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.10
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• pp.373-380
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• 2022

This paper presents a deep learning based group synchronization that supports networked immersive interactions between remote users. The goal of group synchronization is to enable all participants to synchronously interact with others for increasing user presence Most previous methods focus on NTP-based clock synchronization to enhance time accuracy. Moving average filters are used to control media playout time on the synchronization server. As an example, the exponentially weighted moving average(EWMA) would be able to track and estimate accurate playout time if the changes in input data are not significant. However it needs more time to be stable for any given change over time due to codec and system loads or fluctuations in network status. To tackle this problem, this work proposes the Deep Group Synchronization(DeepGroupSync), a group synchronization based on deep learning that models important features from the data. This model consists of two Gated Recurrent Unit(GRU) layers and one fully-connected layer, which predicts an optimal playout time by utilizing the sequential playout delays. The experiments are conducted with an existing method that uses the EWMA and the proposed method that uses the DeepGroupSync. The results show that the proposed method are more robust against unpredictable or rapid network condition changes than the existing method.