• Transactions of the Korean Society of Automotive Engineers
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• v.9 no.5
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• pp.122-130
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• 2001

In this paper, an operational algorithm for a 2-shaft parallel hybrid electric vehicle is suggested for the minimization of operation cost. The operation cost is obtained as a summation of the engine fuel cost and the motor electricity cost. The electrical cost function is estimated in case of motoring, and generating when the recuperation is carried out during the braking. In addition, weight function is introduced in order to maintain the battery state of charge. Based on the operation algorithm, the optimal engine operation point that minimizes the operation cost is obtained with respect to the required vehicle power for every state of charge of battery. The optimal operation point provides the optimal power distribution of the engine and the motor for a required vehicle power Simulation was performed and the fuel economy of the hybrid vehicle was compared to that of the conventional vehicle. Simulation results showed that hybrid vehicle's fuel economy can be improved as much as 45∼48% compared to the conventional vehicle's.

• Transactions of the Korean Society of Automotive Engineers
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• v.7 no.6
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• pp.182-197
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• 1999

Described in this paper is an optimal driving control algorithm which focused on the improvement of fuel economy and the minimization of pollutant emissions in the parallel type hybrid drivertrain system for transit bus. For the energy balance among components such as engine, induction machine and buttery, the algorithm for power split ration determine is proposed. When it is implemented in the hybrid electric control unit(HECU) , using the sub-optimal method and the approximate technique , it is possible to save the memory , to shorten the calculation time, and to achieve the efficient driving actually. A Shift strategy for automated manual transmission is the other side of the driving control algorithm. It enables to select the optimal gear by using several shift maps which were predefined from the proposed method in this paper, As a results of driving simulation, it is proved that these algorithms make the hybrid drivetrain system to reduce fuel consumption and emissions considerably and to have the ability to the efficient use of battery.

• Journal of the Institute of Convergence Signal Processing
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• v.16 no.1
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• pp.1-8
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• 2015

In many image processing tasks, connected component labeling (CCL) is performed to extract regions of interest. CCL was usually done in a sequential fashion when image resolution was relatively low and there are small number of input channels. As image resolution gets higher up to HD or Full HD and as the number of input channels increases, sequential CCL is too time-consuming to be used in real time applications. To cope with this situation, parallel CCL framework was introduced where multiple cores are utilized simultaneously. Several parallel CCL methods have been proposed in the literature. Among them are NSZ label equivalence (NSZ-LE) method[1], modified 8 directional label selection (M8DLS) method[2], and HYBRID1 method[3]. Soh [3] showed that HYBRID1 outperforms NSZ-LE and M8DLS, and argued that HYBRID1 is by far the best. In this paper we propose an improved hybrid parallel CCL algorithm termed as HYBRID2 that hybridizes M8DLS with label backtracking (LB) and show that it runs around 20% faster than HYBRID1 for various kinds of images.

• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.4
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• pp.566-578
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• 2017

For optimizing the operation plan when strike packages attack multiple targets, this article suggests a new mathematical model and a parallel hybrid genetic algorithm (PHGA) as a solution methodology. In the model, a package can assault multiple targets on a sortie and permitted the use of mixed munitions for a target. Furthermore, because the survival probability of a package depends on a flight route, it is formulated as a mixed integer programming which is synthesized the models for vehicle routing and weapon-target assignment. The hybrid strategy of the solution method (PHGA) is also implemented by the separation of functions of a GA and an exact solution method using ILOG CPLEX. The GA searches the flight routes of packages, and CPLEX assigns the munitions of a package to the targets on its way. The parallelism enhances the likelihood seeking the optimal solution via the collaboration among the HGAs.

• Nuclear Engineering and Technology
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• v.54 no.10
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• pp.3864-3877
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• 2022

We propose a new approach called PESA (Prioritized replay Evolutionary and Swarm Algorithms) combining prioritized replay of reinforcement learning with hybrid evolutionary algorithms. PESA hybridizes different evolutionary and swarm algorithms such as particle swarm optimization, evolution strategies, simulated annealing, and differential evolution, with a modular approach to account for other algorithms. PESA hybridizes three algorithms by storing their solutions in a shared replay memory, then applying prioritized replay to redistribute data between the integral algorithms in frequent form based on their fitness and priority values, which significantly enhances sample diversity and algorithm exploration. Additionally, greedy replay is used implicitly to improve PESA exploitation close to the end of evolution. PESA features in balancing exploration and exploitation during search and the parallel computing result in an agnostic excellent performance over a wide range of experiments and problems presented in this work. PESA also shows very good scalability with number of processors in solving an expensive problem of optimizing nuclear fuel in nuclear power plants. PESA's competitive performance and modularity over all experiments allow it to join the family of evolutionary algorithms as a new hybrid algorithm; unleashing the power of parallel computing for expensive optimization.

• Transactions of the Korean Society of Automotive Engineers
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• v.8 no.1
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• pp.72-84
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• 2000

To analyze the correlation between drivetrain systems and to optimize the vehicle design with satisfying of the initial design objects, the performance sensitivity analysis through the iterative design procedure must be carried out. In this study, effects of the design parameters for the main components of the parallel type hybrid drivetrain system are analyzed by using the developed method of the vehicle performance simulation, and the basis of the optimal selection of the design parameters from the relation of design constraints and required performances is suggested. In driving control of the parallel hybrid vehicle, power split ratio is the most important factor, and the improved drivetrain system can be constructed through the only change of the algorithm for determination of the power spilt ratio, which is strongly applicable to the driving patterns and the environments. Therefore, Various techniques, such as the change of the weighting factors and the range extended algorithm, are suggested and evaluated in this paper.

• Proceedings of the KIEE Conference
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• summer
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• pp.36-38
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• 2004

This paper presents an application of parallel hybrid Genetic Algorithm-Tabu Search (GA-TS) algorithm to search an optimal solution of a recokiguration in distribution system. In parallel hybrid CA-TS, after CA operations, stings which are not emerged in the past population are selected in the reproduction procedure. After reproduction operation, if there are many strings which are in the past population, we add new random strings into the population, if there's no improvement for the predetermined iteration, local search procedure is executed by TS for the strings with high fitness function value. To show the usefulness of the proposed method, developed algorithm has been tested and compared on a distribution system in the reference paper.

• Proceedings of the KIEE Conference
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• summer
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• pp.446-448
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• 2004

This paper presents an application of parallel hybrid Genetic Algorithm-Tabu Search (GA-TS) algorithm to search an optimal solution of a service restoration in distribution system. In parallel hybrid GA-TS, after GA operations, strings which are not emerged in the past population are selected in the reproduction procedure. After reproduction operation, if there are many strings which are in the past population, we add new random strings into the population. If there's no improvement for the predetermined iteration, local search procedure is executed by f for the strings with high fitness function value. To show the usefulness of the proposed method, developed algorithm has been tested and compared on a practical distribution system in Korea.

• Transactions of the Korean Society of Automotive Engineers
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• v.19 no.2
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• pp.26-34
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• 2011

Hybrid powertrain systems have been developed to improve the fuel efficiency of internal combustion engines. In the case of a parallel hybrid powertrain system, an engine and a motor are directly coupled. Because of the hardware configuration of the parallel hybrid system, friction and the pumping losses of internal combustion engines always exists. Such losses are the primary factors that result in the deterioration of fuel efficiency in the parallel-type hybrid powertrain system. In particular, the engine operates as a power consumption device during the fuel-cut condition. In order to improve the fuel efficiency for the parallel-type hybrid system, cylinder deactivation (CDA) technology was developed. Cylinder deactivation technology can improve fuel efficiency by reducing pumping losses during the fuel-cut driving condition. In a CDA engine, there are two operating modes: a CDA mode and an SI mode according to the vehicle operating condition. However, during the mode change from CDA to SI, a serious fluctuation of the air-fuel ratio can occur without adequate control. In this study, an air-fuel ratio control algorithm during the mode transition from CDA to SI was proposed. The control algorithm was developed based on the mean value CDA engine model. Finally, the performance of the control algorithm was validated by various engine experiments.

• Journal of Mechanical Science and Technology
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• v.16 no.10
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• pp.1287-1295
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• 2002

In this work, the fuel economy of a parallel hybrid electric vehicle is investigated. A vehicle control algorithm which yields operating points where operational cost of HEV is minimal is suggested. The operational cost of HEV is decided considering both the cost of fossil fuel consumed by an engine and the cost of electricity consumed by an electric motor. A procedure for obtaining the operating points of minimal fuel consumption is introduced. Simulations are carried out for 3 variations of HEV and the results are compared to the fuel economy of a conventional vehicle in order to investigate the effect of hybridization. Simulation results show that HEV with the vehicle control algorithm suggested in this work has a fuel economy 45% better than the conventional vehicle if braking energy is recuperated fully by regeneration and idling of the engine is eliminated. The vehicle modification is also investigated to obtain the target fuel economy set in PNGV program.