• 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.

• 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.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.105-111
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• 2003

This paper is researching the storing of 40 different kinds of conditions. Such as, car speed, delay in starting time and the volume of cars in traffic. Through the use of a central nervous networking system or AI, using 10 different intersecting roads. We will improve the green traffic light. And allow more cars to easily flow through the intersections. Now days, with increasing many vehicles on restricted roads, the conventional traffic light creates prove startup-delay time and end-lag-time. The conventional traffic light loses the function of optimal cycle. And so, 30-45% of conventional traffic cycle is not matched to the present traffic cycle. In this paper proposes electro sensitive traffic light using fuzzy look up table method which will reduce the average vehicle waiting time and improve average vehicle speed. Computer simulation results prove that reducing the average vehicle waiting time which proposed considering passing vehicle length for optimal traffic cycle is better than fixed signal method which dosen't consider vehicle length.

• 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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.04a
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• pp.161-165
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• 2005

In order to produce an optimal traffic cycle. We must first check how many waiting cars are at the lower intersection, because waiting queue is bigger than the length of upper traffic intersection. Start up delay time and vehicle waiting time occurs. To reduce vehicle waiting time, in this paper, we present an optimal green time algorithm using fuzzy neural network. Through computer simulation has been proven to be improved average vehicle speed than fixed traffic signal light which do not consider different intersection conditions.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.4
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• pp.73-83
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• 2004

With increasing numbers of vehicles on restricted roads, It happens that we have much wasted time and decreased average car speed. 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, franc 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 electro-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 md neural network as a preprocessing. Also, we developed an Intelligent PLC(Programmable Logic Controller) for real time traffic forecasting as a postprocesing about unexpectable conditions. Computer simulation results proved reducing average vehicle waiting time which proposed coordinating green time better than electro-sensitive franc light system does not consider coordinating green time.

• Journal of Microbiology and Biotechnology
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• v.16 no.6
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• pp.863-869
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• 2006

The behavior of submerged membrane bioreactor (SMBR) filtration systems utilizing rapid air backpulsing as a cleaning technique to remove reversible foulants was investigated using a genetic algorithm (GA). A customized genetic algorithm with suitable genetic operators was used to generate optimal time profiles. From experiments utilizing short and long periods of forward and reverse filtration, various experimental process parameters were determined. The GA indicated that the optimal values for the net flux fell between 263-270 LMH when the forward filtration time ($t_f$) was 30-37 s and the backward filtration time ($t_b$) was 0.19-0.27 s. The experimental data confirmed the optimal backpulse duration and frequency that maximized the net flux, which represented a four-fold improvement in 24-h backpulsing experiments compared with the absence of backpulsing. Consequently, the identification of a region of feasible parameters and nonlinear flux optimization were both successfully performed by the genetic algorithm, meaning the genetic algorithm-based optimization proved to be useful for solving SMBR flux optimization problems.

• Journal of the Korea Computer Industry Society
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• v.5 no.9
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• pp.1005-1016
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• 2004

The conventional traffic light loses the function of optimal traffic signal cycle. And so, 30-45% of conventional traffic signal cycle is not matched to the present traffic signal cycle. In this paper proposes electro sensitive traffic light using fuzzy rules which will reduce the average vehicle waiting time and improve average vehicle speed. This paper is researching the storing method of 40 different kinds of sensor input conditions. Such as, car speed, delay· in starting time and the volume of cars in the real traffic situation. It will estimate the optimal green time in the 10 different intersections using Intelligent fuzzy method. Computer simulation results prove that reducing the average vehicle waiting time and offset better than fixed signal method which doesn't consider vehicle length.

• The Korean Journal of Food And Nutrition
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• v.25 no.1
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• pp.77-82
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• 2012

This study was carried out to optimize the production of immature green cherry tomato pickles and to produce green cherry tomato pickles of good sensory quality. The composition of immature green cherry tomato pickles was optimized using a central composition design with 3 variables and 3 levels. The overall acceptability score, based on sensory evaluation, was best, when the immature green cherry tomato pickles contained 231 g of vinegar, 52.6 g of salt, 168.3 g of sugar, 204 g of tomatoes, and 231 $m{\ell}$ of water. The statically predicted optimal formulation of immature green cherry tomato pickles on overall acceptability value was 33.54%(w/w) of vinegar, 7.64%(w/w) of salt, 25.28%(w/w) of sucrose, 33.54%(w/w) of water. The optimal conditions for producing immature green cherry tomato pickles should consider the factors of time and temperature of storage.

• 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%.