• Journal of the Korean Society of Industry Convergence
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• v.4 no.1
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• pp.41-49
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• 2001

This study developed signal optimization algorithm by analyzing vehicle arrival patterns. The major principle of signal optimization is dissipate all queueing vehicle in 1cycle and assign delay time uniformly for all approaches. For this, this study used optimal green time and surplus green time. Optimal green time calculated by estimated traffic volume from vehicle arrival model. Surplus green time defined as the gap of optimal green time and queue dissipated time. And alternative cycle has minimum surplus green time was selected as the optimal cycle. Finally, total delay and average delay per vehicle can be calculated by using queueing theory.

• Asian Journal of Turfgrass Science
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• v.13 no.3
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• pp.139-146
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• 1999

This studies was carried out to estimate the influence of mowing height, time lapse, rolling, and dew removal on green speed of putting green in Lake Side C. C. on 29, 30 Jun. 1998. The results were as follows. As mowing height increased, green speed tended to be decreased. After mowing, green speed tended to be decreased over the time, and appeared to be decrease significantly on the next day[Y=3.206-0.127.X1-1.41$\times$10-2.X4(Y=green speed, X1=mowing height, X4=time lapse)]. This suggests that the frequency of mowing must be increased to maintain the green speed. But, frequent mowing cause the turfgrass of putting green to be stressed. Rolling tended to increase green speed[Y=3.555-0.202.X1+0.111.X2(Y=green speed, X1=mowing height, X2=rolling)]. Thus, rollers is thought to be an tool used to increase green speed and rolling is expected to be able to decrease turfgrass stress while maintaining the performance level of the putting green. Dew removal appeared to increase green speed significantly[Y=2.499-0.125.X1+0.366.X3(Y=green speed, X1=mowing height, X3=dew removal)]. Thus, dew removal is expected to maintain the green speed in the morning.

• Journal of Korean Society of Transportation
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• v.12 no.1
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• pp.55-73
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• 1994

The objective of this research is to evaluate the pedestrian signal time involving green and flashing green times. The minimum pedestrian green indication should give time for pedestrian to start crossing safely, and the flashing green indication should give time to complete the crossing. An average pedestrian crossing speed of 1.1(m/s) was estimated by analyzing the field data which was slower than the 1.2(m/s) currently used. Furthermore, the study proposed that design speed for the flashing green time should be slow speed for considerations pedestrian safety, not the average speed. The 0.78-1.01(m/s) of pedestrian speed was estimated at the elementary school areas that indicated 0.2(m/s) slower than the other areas. The pedestrian starting time (perception/reaction time) and time headway from front to back of herd was estimated to determine minimum pedestrian green time. the pedestrian starting time was estimated to determine minimum pedestrian green time. The pedestrian starting time was ranged 2.52-4.29 seconds. The time interval between the pedestrian rows was found to be 1.25-1.86 seconds, which declines as the pedestrian rows increases, The equation to calculate the pedestrian signal, which declines as the pedestrian rows increases. The equation to calculate the pedestrian signal time is proposed using the pedestrian starting time, the time interval between the pedestrian rows, and pedestrian crossing speed given area types (commercial, business, mixed, and elementary school areas), number of both-directional pedestrians for a cycle, crosswalk length and width.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.4
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• pp.330-337
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• 2000

This paper proposes a new concept of coordinating green this which controls 10 traffic intersection systems. For instance, if we have a baseballs game at 8 pm today, traffic volume toward the baseball game at 8 pm today, traffic volume toward the baseball game will be incr eased 1 hour or 1 hour 30 minutes before the baseball game. at that time we can not pred ict optimal green time Even though there have smart elctrosensitive traffic light system. Therefore, in this paper to improve average vehicle speed and reduce average vehicle waiting time, we created optimal green time using fuzzy rules and neural network. Computer simulation results proved reducing average vehicle waiting time proposed coordinating green time better than electro-sensitive traffic light system. Therefore, in this paper to improvevehicle speed and reduce average vehicle waiting time, we created optiual green time fuzzy rules and neural network. Computer simulation results proved reducing average vehicle waiting time which proposed coordinating green time better than electro-sensitive traffic light system dosen't consider coordinating green time.

• Journal of the Korean Society of Food Science and Nutrition
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• v.19 no.3
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• pp.195-200
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• 1990

Effects of cooking time(5-30 min in a pressure cooker) and HTST air dehydratiion time(0-9min at 15$0^{\circ}C$) on physical properties of dried green peas(3% oisture content wet basis) were investigated by determining rehydration ratio rehydration curve browning reaction and puncture force, The rehydration ratio and curve of dried green peas were increased with increa-sing cooking time and HTST air dehydration time. Preheating of the green peas for 30 min in a pressure cooker or for 9 min of HTST air dehydration time prior to 6$0^{\circ}C$ air dehydration recovered a 87.3% of original moisture content of raw green peas in a boiling water for 5 min. The brownin greaction was gradually decreased up to 15 min of cooking time. Puncture pressure of rehydrated green peas treated in a boiling water for 5 min was decreased as the cooking time and HTST air dehydration time were increased and was highly correlated with rehydration (r=-0.956) The effects of cooking time and HTST air dehydration time on rehydration ratio browning reaction and puncture pressure were significantly different at the a=0.01 level except effect of HTST air dehydration time on browning reaction.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.1
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• pp.15-25
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• 2014

Bus signal priority is a name for various techniques to speed up bus public transport services at intersections with traffic signals. In this study propose methodology to optimize signal times for Early green, Green extension out of the active bus signal priority using deterministic delay model in isolated intersection on median bus lane. Fluctuation is found in the vehicle delay and person delay in the event that using this methodology redistributed to green time and checking slack green time is correct value by sensitivity analysis. As a result of the study, car delay is increased a little and person delay is decreased. As a result of slack green time sensitivity, delay is not much in it if variation of slack green time under 30%. But this methodology effectiveness is under claimed capacity if variation of slack green time over 30%.

• Journal of the Society of Naval Architects of Korea
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• v.45 no.3
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• pp.258-268
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• 2008

The radiation potential of a ship advancing in waves is studied using the 3D time-domain forward-speed free-surface Green function and the Green integral equation. Numerical solutions are obtained by making use of the 2nd order BEM(Boundary Element Method) which make it possible to take account of the line integral along the waterline in a rigorous manner. The 6 degree of freedom motion memory functions of a hemisphere and the Wigley seakeeping model obtained by direct integration of the time-domain 3D potentials over the wetted surface are presented for various Froude numbers.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2000.04a
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• pp.23-28
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• 2000

The Green integral equation for the calculation of the forward-speed time-harmonic radiation-diffraction potentials IS derived. The forward-speed Green function presented by Brard is used and the correct free surface boundary condition for the Green function is imposed. The cause of the mistakes in the existing Green integral equation is also pointed out.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.292-297
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• 2000

This paper proposes a new concept of coordinating green time which controls 10 traffic intersection systems. For instance, if we have a baseball game at 8 pm today, traffic volume toward the baseball game at 8 pm today, traffic volume toward the baseball game will be increased 1 hour or 1 hour and 30 minutes before the baseball game. At that time we can not predict optimal green time Even though there have smart elctro-sensitive traffic light system. Therefore, in this paper to improve average vehicle speed and reduce average vehicle waiting time, we created optimal green time using fuzzy rules and neural network. Computer simulation results proved reducing average vehicle waiting time which proposed coordinating green time better than electro-sensitive traffic light system dosen't consider coordinating green time.

• Journal of Environmental Science International
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• v.24 no.5
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• pp.647-655
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• 2015

In this study, to targeting Songpa of Seoul, were analyzed the effect on the noise mitigation in roadside buffer green spaces. Noise of Songpa Street buffer green space was determined to be higher during the day than at night. In addition, it was most of 60 db or more. However, the noise reduction function of the buffer green was not greatly affected by time. In the case of noise reduction rate, during the day time it was the order of the mounding type (18.14%)> plain type (5.73%)> slope type (4.08%), And, in the case of night time, it was the order of the mounding type (11.29%)> slope type (10.22%)> plain type (4.42%). Noise reduction rate, all of the daytime, was the highest in the mounding type. As a result of the factors on the amount of reduction of noise, More physical structure is mounding type, green structure is the stratification of green space, and the number of individuals is large, the higher the tree planting density, it is determined that the noise reduction effect is high. Also, factors affecting the noise reduction effect of the day and night were different.