• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.3
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• pp.51-58
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• 2004

Optimization of engine desist and operation parameters using a genetic algorithm was demonstrated for direct injection diesel engine combustion. A micro genetic algorithm and a modified KIVA-3V code were used for the analysis and optimization of the engine combustion. At each generation of the optimization step the micro genetic algorithm generated five groups of parameter sets, and the five cases of KIVA-3V analysis were to be performed either in series or in parallel. The micro genetic algorithm code was also parallelized by using MPI programming, and a multi-CPU parallel supercomputer was used to speed up the optimization process by four times. An example case for a fixed engine speed was performed with six parameters of intake swirl ratio, compression ratio, fuel injection included angle, injector hole number, SOI, and injection duration. A simultaneous optimization technique for the whole range of engine speeds would be suggested for further studies.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.8-19
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• 2023

This paper presents advancement in multi- unmanned aerial vehicle (UAV) cooperative area surveillance, focusing on optimizing UAV route planning through the application of genetic algorithms. Addressing the complexities of comprehensive coverage, two real-time dynamic path planning methods are introduced, leveraging genetic algorithms to enhance surveillance efficiency while accounting for flight constraints. These methodologies adapt multi-UAV routes by encoding turning angles and employing coverage-driven fitness functions, facilitating real-time monitoring optimization. The paper introduces a novel path planning model for scenarios where UAVs navigate collaboratively without predetermined destinations during regional surveillance. Empirical evaluations confirm the effectiveness of the proposed methods, showcasing improved coverage and heightened efficiency in multi-UAV path planning. Furthermore, we introduce innovative optimization strategies, (Foresightedness and Multi-step) offering distinct trade-offs between solution quality and computational time. This research contributes innovative solutions to the intricate challenges of cooperative area surveillance, showcasing the transformative potential of genetic algorithms in multi-UAV technology. By enabling smarter route planning, these methods underscore the feasibility of more efficient, adaptable, and intelligent cooperative surveillance missions.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.82-87
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• 1994

In this study, dynamic optimal design far a two degree-of-freedom anthropomorphic robot module is performed. Several dynamic design indices associated with the inertia matrix and the inertia power array are introduced. Analysis for the relationship between the dynamic parameters and the design indices shows that trade-offs exist between the isotropy and the dynamic design indices related to the actuator size. A composite design index is employed to deal with multi-criteria based design with different weighting factors, in a systematic manner. We demonstrate the fact that dynamic optimization is another significant step to enhance the system performances, followed by kinematic optimization.

• Nuclear Engineering and Technology
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• v.53 no.12
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• pp.4166-4178
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• 2021

The Control Rod Drive Mechanism (CRDM) is an essential part of the reactor, which realizes the start-stop and power adjustment of the reactor by lifting and lowering the control rod assembly. As a moving part in CRDM, the latch directly contacts with the control rod assembly, and the life of latch is closely related to the service life of the reactor. In this paper, the relationship between the life of the latch and the step stress, friction stress, and impact stress in the process of movement is analyzed, and the optimization methodology and process of latch life based on the approximate model are proposed. The design variables that affect the life of the latch are studied through the experimental design, and the optimization objective of design variables based on the latch life is established. Based on this, an approximate model of the life of the latch is built, and the multi-objective optimization of the life of the latch is optimized through the NSGA-II algorithm.

• Journal of Electrical Engineering and information Science
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• v.2 no.6
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• pp.95-105
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• 1997

In this paper, we have studied about design and fabrication of the actively shielded superconducting MRI magnet. Nonlinear optimization methods are usually used to find optimum coil configurations. However the selection of initial coil configurations is very difficult. In case bad initial data are used, it is even impossible to find optimum coil configurations which satisfy predefined constraints. We have developed computer optimization program which consists of two steps. Initial coil configurations are easily selected through linear optimization in the first step and optimum coil configurations are found through nonlinear optimization in the second step. We have also studied about superconducting shim coils to cancel error fields caused by coil fabrication errors. Many researchers published design concepts of shim coil. However all these studies are for shim coil design using filamentary coils with single turn, Shim coils with multi-turns should be used to produce enough field strength to cancel error fields. We have developed computer program for the design of shim coils which have proper thickness and length. An actively shielded superconducting MRI magnet with a small warm bore was fabricated and four sets of superconducting shim coils were equipped. The magnetic field distributions were measured and field correction was carried out using shim coils.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.742-751
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• 2018

Electric vehicles (EV) are emerging as the future transportation vehicle reflecting their potential safe environmental advantages. Vehicle to Grid (V2G) system describes the hybrid system in which the EV can communicate with the utility grid and the energy flows with insignificant effect between the utility grid and the EV. The paper presents an optimal power control and energy management strategy for Plug-In Electric Vehicle (PEV) charging stations using Wind-PV-FC-Battery renewable energy sources. The energy management optimization is structured and solved using Multi-Objective Particle Swarm Optimization (MOPSO) to determine and distribute at each time step the charging power among all accessible vehicles. The Model-Based Predictive (MPC) control strategy is used to plan PEV charging energy to increase the utilization of the wind, the FC and solar energy, decrease power taken from the power grid, and fulfil the charging power requirement of all vehicles. Desired features for EV battery chargers such as the near unity power factor with negligible harmonics for the ac source, well-regulated charging current for the battery, maximum output power, high efficiency, and high reliability are fully confirmed by the proposed solution.

• Nuclear Engineering and Technology
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• v.55 no.5
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• pp.1838-1854
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• 2023

This paper presents a hybrid algorithm to solve the multi-objective path planning (MOPP) problem for mobile robots in a static nuclear accident environment. The proposed algorithm mimics a real nuclear accident site by modeling the environment with a two-layer cost grid map based on geometric modeling and Monte Carlo calculations. The proposed algorithm consists of two steps. The first step optimizes a path by the hybridization of improved ant colony optimization algorithm-modified A^* (IACO-A^*) that minimizes path length, cumulative radiation dose and energy consumption. The second module is the high radiation dose rate avoidance strategy integrated with the IACO-A^* algorithm, which will work when the mobile robots sense the lethal radiation dose rate, avoiding radioactive sources with high dose levels. Simulations have been performed under environments of different complexity to evaluate the efficiency of the proposed algorithm, and the results show that IACO-A^* has better path quality than ACO and IACO. In addition, a study comparing the proposed IACO-A^* algorithm and recent path planning (PP) methods in three scenarios has been performed. The simulation results show that the proposed IACO-A^* IACO-A^* algorithm is obviously superior in terms of stability and minimization the total cost of MOPP.

• Journal of Mechanical Science and Technology
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• v.14 no.9
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• pp.1005-1012
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• 2000

Numerical optimization techniques combined with a three-dimensional thin-layer Navier-Stokes solver are presented to find an optimum shape of a stator blade in an axial compressor through calculations of single stage rotor-stator flow. Governing differential equations are discretized using an explicit finite difference method and solved by a multi-stage Runge-Kutta scheme. Baldwin-Lomax model is chosen to describe turbulence. A spatially-varying time-step and an implicit residual smoothing are used to accelerate convergence. A steady mixing approach is used to pass information between stator and rotor blades. For numerical optimization, searching direction is found by the steepest decent and conjugate direction methods, and the golden section method is used to determine optimum moving distance along the searching direction. The object of present optimization is to maximize efficiency. An optimum stacking line is found to design a custom-tailored 3-dimensional blade for maximum efficiency with the other parameters fixed.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.19 no.2 s.72
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• pp.115-124
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• 2006

The 1st and 2nd tension forces of the PSSC(Prestressed Steel and Concrete) grider constructed with commercial rolling beams and concrete are optimally designed. The design variables are the 1st and 2nd tension forces due to multi-step prestressing and live load. The objective function is set to the maximum live load. Design conditions are allowable stresses at the top and bottom of slab, beam and infilled concrete due at the several construction stages. A Matlab based optimization program is developed. The results show that the tendon position as well as concrete compression strength have significant influence on the beam strength.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1579-1602
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• 2020

In the design of production system, buffer capacity allocation is a major step. Through polymorphism analysis of production capacity and production capability, this paper investigates a buffer allocation optimization problem aiming at the multi-stage production line including unreliable machines, which is concerned with maximizing the system theoretical production rate and minimizing the system state entropy for a certain amount of buffers simultaneously. Stochastic process analysis is employed to establish Markov models for repairable modular machines. Considering the complex structure, an improved vector UGF (Universal Generating Function) technique and composition operators are introduced to construct the system model. Then the measures to assess the system's multi-state reliability and structural complexity are given. Based on system theoretical production rate and system state entropy, mathematical model for buffer capacity optimization is built and optimized by a specific genetic algorithm. The feasibility and effectiveness of the proposed method is verified by an application of an engine head production line.