• 한국유체기계학회 논문집
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• 제14권5호
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• pp.31-38
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• 2011

Optimization using a hybrid multi-objective evolutionary algorithm coupled with response surface approximation has been performed to improve the performance of a transonic axial compressor with circumferential casing grooves. In order to optimize the operating stability and peak adiabatic efficiency of the compressor with circumferential casing grooves, tip clearance, angle distribution at blade tip and the depth of the circumferential casing grooves are selected as design variables. Three-dimensional Reynolds-averaged Navier-Stokes equations with the shear stress transport turbulence model are discretized by finite volume approximations. The trade-off between two objectives with the interaction of blade and casing treatment is determined and discussed with respect to the representative clusters in the Pareto-optimal solutions compared to the axial compressor without the casing treatment.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 A
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• pp.194-196
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• 2001

This paper presents a hybrid algorithm for solving security constrained OPF in interconnected power systems, which is based on the combined application of evolutionary programming (EP) and sequential quadratic programming (SQP). The objective functions are the minimization of generation fuel costs and system power losses. In OPF considering security, the outages are selected by contingency ranking method. The control variables are the active power of the generating units, the voltage magnitude of the generator, transformer tap settings and SVC setting. The state variables are the bus voltage magnitude, the reactive power of the generating unit, line flows and the tie line flow. The method proposed is applied to the modified IEEE 14buses model system.

• Smart Structures and Systems
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• 제25권4호
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• pp.487-499
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• 2020

This paper proposes a novel approach to model updating for a large-scale cable-stayed bridge based on ambient vibration tests coupled with a hybrid metaheuristic search algorithm. Vibration measurements are carried out under excitation sources of passing vehicles and wind. Based on the measured structural dynamic characteristics, a finite element (FE) model is updated. For long-span bridges, ambient vibration test (AVT) is the most effective vibration testing technique because ambient excitation is freely available, whereas a forced vibration test (FVT) requires considerable efforts to install actuators such as shakers to produce measurable responses. Particle swarm optimization (PSO) is a famous metaheuristic algorithm applied successfully in numerous fields over the last decades. However, PSO has big drawbacks that may decrease its efficiency in tackling the optimization problems. A possible drawback of PSO is premature convergence leading to low convergence level, particularly in complicated multi-peak search issues. On the other hand, PSO not only depends crucially on the quality of initial populations, but also it is impossible to improve the quality of new generations. If the positions of initial particles are far from the global best, it may be difficult to seek the best solution. To overcome the drawbacks of PSO, we propose a hybrid algorithm combining GA with an improved PSO (HGAIPSO). Two striking characteristics of HGAIPSO are briefly described as follows: (1) because of possessing crossover and mutation operators, GA is applied to generate the initial elite populations and (2) those populations are then employed to seek the best solution based on the global search capacity of IPSO that can tackle the problem of premature convergence of PSO. The results show that HGAIPSO not only identifies uncertain parameters of the considered bridge accurately, but also outperforms than PSO, improved PSO (IPSO), and a combination of GA and PSO (HGAPSO) in terms of convergence level and accuracy.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.158-161
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• 2003

A cascade structured neural network called Sigma-Pi$_{t}$ Cascaded Hybrid Neural Network ($\sigma$$\pi$$_{t}$-CHNN) is Proposed. It is an extended version of the Sigma-Pi Cascaded extended Hybrid Neural Network ($\sigma$$\pi$-CHNN), where the classical multiplicative neuron ($\pi$-neuron) is replaced by the translated multiplicative ($\pi$$_{t}$-neuron) model. The learning algorithm of $\sigma$$\pi$$_{t}$-CHNN is composed of an evolutionary programming method, responsible for determining the network architecture, and of a Levenberg-Marquadt algorithm, responsible for tuning the weights of the network. The $\sigma$$\pi$$_{t}$-CHNN is evaluated in 2 pattern classification problems: the 2 spirals and the sonar problems. In the 2 spirals problem, $\sigma$$\pi$$_{t}$-CHNN can generate neural networks with 10％ less hidden neurons than that in previous neural models. In the sonar problem, $\sigma$$\pi$$_{t}$-CHNN can find the optimal solution for the problem i.e., a network with no hidden neurons. These results confirm the expanded information processing capabilities of $\sigma$$\pi$$_{t}$-CHNN, when compared to previous neural network models. network models.

• 대한간호학회지
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• 제50권2호
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• pp.178-190
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• 2020

Purpose: The purpose of this study was to identify trends of nursing concept development in Korean journal papers to improve accurate understanding of nursing concepts. Methods: A systematic review of 216 concept development articles published from 1970 to 2018 that met the inclusion criteria was conducted using Research Information Sharing Service (RISS) databases. Results: The most common method of concept development was Walker and Avant's concept analysis method, identified in 139 (64.3%) of the 216 studies, followed by 48 examples of hybrid models (22.2%) and 15 examples of evolutionary methods (6.9%). Chinn and Kramer's method, Norris's clarification, Wilson's method, and others were also used. The concepts of "spirituality" and "fatigue" were most frequently analyzed. Among the 139 studies that used Walker and Avant's concept analysis method, 127 studies (91.4%) applied all the recommended steps; the others applied the recommended steps partially, omitting description of model cases/additional cases, antecedents/consequences, and empirical indicators. Among the studies using the hybrid model, among two (5.7%) did not describe attributes, three (8.5%) did not provide definitions, and 16 (45.7%) did not present empirical indicators in the final stage. Conclusion: Among concept development studies published in Korean journals, Walker and Avant's concept analysis method is most commonly used. In case of most studies using Walker and Avant's method a suitable concept analysis process is applied, but in case of other studies using the other concept development method, a suitable concept analysis process is not applied. Therefore, a suitable concept analysis process must be applied for concept development in nursing research.

• 한국경영과학회지
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• 제41권3호
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• pp.75-96
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• 2016

This paper investigates the usefulness of drones in an urban delivery system. We define the vehicle and drone routing problem with time window (VDRPTW) and present a model that can describe a dual mode delivery system consisting of drones and vehicles in the metropolitan area. Drones are relatively free from traffic congestion but have limited flight range and capacity. Vehicles are not free from traffic congestion, and the complexity of urban road network reduces the efficiency of vehicles. Using drones and vehicles together can reduce inefficiency of the urban delivery system because of their complementary cooperation. In this paper, we assume that drones operate in a point-to-point manner between the depot and customers, and that customers in the need of fast delivery are willing to pay additional charges. For the experiment datasets, we use instances of Solomon (1987), which are well known in the Vehicle Routing Problem society. Moreover, to mirror the urban logistics demand trend, customers who want fast delivery are added to the Solomon's instances. We propose a hybrid evolutionary algorithm for solving VDRPTW. The experiment results provide different useful insights according to the geographical distributions of customers. In the instances where customers are randomly located and in instances where some customers are randomly located while others form some clusters, the dual mode delivery system displays lower total cost and higher customer satisfaction. In instances with clustered customers, the dual mode delivery system exhibits narrow competition for the total cost with the delivery system that uses only vehicles. In this case, using drones and vehicles together can reduce the level of dissatisfaction of customers who take their cargo over the time-window. From the view point of strategic flexibility, the dual mode delivery system appears to be more interesting. In meeting the objective of maximizing customer satisfaction, the use of drones and vehicles incurs less cost and requires fewer resources.

• Smart Structures and Systems
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• 제33권1호
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• pp.65-91
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• 2024

The use of five optimization techniques for the prediction of a strength-based concrete mixture's best-fit model is examined in this work. Five optimization techniques are utilized for this purpose: Slime Mold Algorithm (SMA), Black Hole Algorithm (BHA), Multi-Verse Optimizer (MVO), Vortex Search (VS), and Whale Optimization Algorithm (WOA). MATLAB employs a hybrid learning strategy to train an artificial neural network that combines least square estimation with backpropagation. Thus, 72 samples are utilized as training datasets and 31 as testing datasets, totaling 103. The multi-layer perceptron (MLP) is used to analyze all data, and results are verified by comparison. For training datasets in the best-fit models of SMA-MLP, BHA-MLP, MVO-MLP, VS-MLP, and WOA-MLP, the statistical indices of coefficient of determination (R²) in training phase are 0.9603, 0.9679, 0.9827, 0.9841 and 0.9770, and in testing phase are 0.9567, 0.9552, 0.9594, 0.9888 and 0.9695 respectively. In addition, the best-fit structures for training for SMA, BHA, MVO, VS, and WOA (all combined with multilayer perceptron, MLP) are achieved when the term population size was modified to 450, 500, 250, 150, and 500, respectively. Among all the suggested options, VS could offer a stronger prediction network for training MLP.

• 천문학회보
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• 제40권1호
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• pp.59.1-59.1
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• 2015

We present hydrodynamic simulations of gas clouds that inflowing from the disk to a few hundred parsec region of the Milky Way. Realistic Galactic structures are included in our simulations by thousands of multipole expansions that describe 6.4 million stellar particles of a self-consistent Galaxy simulation (Baba, Saitoh & Wada, in prep.). We find that a hybrid multipole expansion model with two different basis sets and a thick disk correction well reproduces the overall structures of the Milky Way. We find that the nuclear ring evolves into 240 pc at T~1500 Myr, regardless of the initial size. For most of simulation runs, gas inflow rate to the nuclear region is equilibrated as ~0.02 Msun/yr, and thus accumulated gas mass and star formation activity is stabilized as $6{\times}10^7Msun$ and ~0.02M/yr, respectively. These stabilized values are in a good agreement with estimations for the CMZ. The nuclear ring is off-centered to the Galactic center by the lopsided central mass distribution of the Galaxy model, and thus an asymmetric mass distribution is arose accordingly. The lopsidedness also leads the nuclear ring to be tilted to the Galactic plane and to precess along the Galaxy rotation. In early evolutionary stage when gas clouds start to inflow and form the nuclear ring, the z-directional oscillations of the gas clouds results in the twisted, infinity-shaped nuclear ring. Since the infinity-shaped feature is transient only for first 100 Myr, the current infinity-shape observed in the CMZ may indicate that the CMZ forms quite recently.