• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.37 no.3
• /
• pp.397-403
• /
• 2013

The demand for eco-friendly and higher fuel economy vehicles has helped develop eco-friendly and fuel-efficient vehicles such as hybrid vehicles. In a hybrid vehicle, the change in the battery charge after driving should be added to the fuel consumption as the equivalent fuel usage based on its own characteristics. Thus, the fuel efficiency of a hybrid vehicle cannot be improved simply by increasing the battery capacity. In this study, I attempt to improve the total fuel economy of a hybrid vehicle, including the equivalent fuel consumption, by modeling a fuel cell hybrid vehicle using Matlab Simulink, analyzing the usage zone of the fuel cell with the existing control strategy, and optimizing the power distribution of the battery and fuel cell in the main usage zone of the fuel cell.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.25 no.5
• /
• pp.883-890
• /
• 2001

In practical design studies, most of designers solve multidisciplinary problems with large size and complex design system. These multidisciplinary problems have hundreds of analysis and thousands of variables. The sequence of process to solve these problems affects the speed of total design cycle. Thus it is very important for designer to reorder the original design processes to minimize total computational cost. This is accomplished by decomposing large multidisciplinary problem into several multidisciplinary analysis subsystem (MDASS) and processing it in parallel. This paper proposes new strategy for parallel decomposition of multidisciplinary problem to raise design efficiency by using genetic algorithm and shows the relationship between decomposition and multidisciplinary design optimization (MDO) methodology.

• Journal of Korean Society for Quality Management
• /
• v.43 no.4
• /
• pp.471-488
• /
• 2015

Purpose: Double sampling $T^2$ chart is a useful tool for detecting a relatively small shift in process mean when the process is controlled by multiple variables. This paper finds the optimal design of the double sampling $T^2$ chart in both economical and statistical sense under Weibull failure model. Methods: The expected cost function is mathematically derived using recursive equation approach. The optimal designs are found using a genetic algorithm for numerical examples and compared to those of single sampling $T^2$ chart. Sensitivity analysis is performed to see the parameter effects. Results: The proposed design outperforms the optimal design of the single sampling $T^2$ chart in terms of the expected cost per unit time and Type-I error rate for all the numerical examples considered. Conclusion: Double sampling $T^2$ chart can be designed to satisfy both economic and statistical requirements under Weibull failure model and the resulting design is better than the single sampling counterpart.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 1997.04a
• /
• pp.413-417
• /
• 1997

Distributed piezoelectric sensor and actuator have been designed for efficient vibration control of a cantilevered beam. Both PZT and PVDF are used in this study, the former as an actuator and the latter as a sensor for our integrated structure. For the PZT actuator, the position and size have been optimized. Optimal electrode shape of the PVDF sensor has been determined. For multi-mode vibration control, we have used two PZT actuators and a PVDF sensor. Electrode shading of PVDF is more powerful for modal force adjustment than the sizing and positioning of PZT. Finite element method is used to model the structure that includes the PZT actuator and the PVDF sensor. By deciding on or off of each PZT segment, the length and the location of the PZT actuator are optimize. Considering both of the host structure and the optimized actuators, it is designed that the active electrode width of PVDF sensor along the span of the beam. Actuator design is based on the criterion of minimizing the system energy in the control modes under a given initial condition. Sensor is designed to minimize the observation spill-over. Modal control forces for the residual(uncontrolled) modes have been minimized during the sensor design. Genetic algorithm, which is suitable for this kind of discrete problems, has been utilized for optimization. Discrete LQG control law has been applied to the integrated structure for real time vibration control. Performance of the sensor, the actuator, and the integrated smart structure has been demonstrated by experiments.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.14 no.8
• /
• pp.757-766
• /
• 2004

This paper focuses on the formulation and validation of an automatic strategy to select the optimal location and direction of strain gauges for the measurement of the modal response. These locations and directions are important to render the strain measurements as robust as possible when a random mispositioning of the gauges and gauge failures are expected. The approach relies on the evaluation of the signal-to-noise ratios of the gauge measurements from strain data of finite element. The multi-step optimization strategy including genetic algorithm is used to find the strain gauge locations-directions that maximize the smallest modal strain signal-to-noise ratio in the absence of gauge failure or its expected value when gauge failure is possible. A flat Plate is used to prove the applicability of the proposed methodology and to demonstrate the effects of the essential parameters of the problem such as the mispositioning level, the probability of gauge failure, and the number of gauges.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2007.05a
• /
• pp.2089-2093
• /
• 2007

저수지군 연계운영 문제는 서로 상충되는 목적들이 존재하고, 다양한 평가 기준들이 존재하는 다목적 특성을 갖는 문제이다. 때문에 저수지군 연계운영 문제에 다중목적계획법이 많이 사용되고 있으나 문제의 해결을 위해 사용한 다수의 목적간의 가중치 설정에 의사결정자의 주관적요소가 반영 될 수도 있고, 설정된 가중치에 따라 결과 값이 민감하게 반응하여 의사결정자가 바람직한 가중치 설정에 어려움이 있다. 본 연구의 목적은 다중 목적 특성이 존재하는 저수지군 연계운영 문제에 다요소 의사결정기법 적용하여 바람직한 저수지별 저수 가중치를 선정하는 방법을 제안하는 것이다. 제안하는 저수 가중치 선정 절차는, 우선 GA-CoMOM (Genetic-Algorithm Coordinate Multi-reservoir Operation Model)을 통해 수계 전체 관점에서 저수량과 발전량의 상충되는 목적에 대한 파레토 최적해와 각 최적해에 해당하는 저수지별 저수 가중치를 도출한다. 다음 단계로 다요소 의사결정기법중에 하나인 수정된 거리척도 기반의 DEA 순위 선정 절차를 이용하여 도출된 최적해들의 운영 결과를 평가하여 파레토 최적해군 중에 선호해를 결정하고, 결정된 선호해의 저수지별 저수 가중치를 해당 기간의 저수 가중치로 선정한다. 설명한 선호 가중치 선정 절차를 금강 수계에 적용해 보고 저수지 연계운영에서 바람직한 가중치를 도출할 수 있음을 보인다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.6 no.9
• /
• pp.2037-2051
• /
• 2012

Cognitive radio (CR) is envisioned as a promising paradigm of exploiting intelligence for enhancing efficiency of underutilized spectrum bands. In CR, the main concern is to reliably sense the presence of primary users (PUs) to attain protection against harmful interference caused by potential spectrum access of secondary users (SUs). In this paper, evolutionary algorithms, namely, particle swarm optimization (PSO) and genetic algorithm (GA) are proposed to minimize the total sensing decision error at the common soft data fusion (SDF) centre of a structurally-centralized cognitive radio network (CRN). Using these techniques, evolutionary operations are invoked to optimize the weighting coefficients applied on the sensing measurement components received from multiple cooperative SUs. The proposed methods are compared with each other as well as with other conventional deterministic algorithms such as maximal ratio combining (MRC) and equal gain combining (EGC). Computer simulations confirm the superiority of the PSO-based scheme over the GA-based and other conventional MRC and EGC schemes in terms of detection performance. In addition, the PSO-based scheme also shows promising convergence performance as compared to the GA-based scheme. This makes PSO an adequate solution to meet real-time requirements.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.14 no.1
• /
• pp.153-158
• /
• 2006

To optimize the combustion system in a HSDI diesel engine, a new analysis technology was developed. The in-cylinder 3-D combustion analysis was carried out by the modified KIVA-3V, and the spray characteristics for the high pressure injection system were analyzed by HYDSIM. The combustion design parameters were optimized by coupling the KIVA-3V and the iSIGHT. The optimization procedure consists of 3 steps. The $1^{st}$ step is the sampling method by the Design of Experiment(DOE), the $2^{nd}$ step is the approximation using the Neural Network method, and the $3^{rd}$ step is the optimization using the Genetic Algorithm. The developed procedures have been approved as very effective and reliable, and the computational results agree well with the experimental data. The analysis results show that the optimized combustion system in a HSDI diesel engine is capable of reducing NOx and Soot emissions simultaneously keeping a same level of the fuel consumption(BSFC).

• International Journal of Aeronautical and Space Sciences
• /
• v.11 no.1
• /
• pp.19-25
• /
• 2010

A computational study for the optimal design of heat exchangers (HX) used in a high temperature and high pressure system is presented. Two types of air to air HX are considered in this study. One is a single-pass cross-flow type with straight plain tubes and the other is a two-pass cross-counter flow type with plain U-tubes. These two types of HX have the staggered arrangement of tubes. The design models are formulated using the number of transfer units ($\varepsilon$-NTU method) and optimized using a genetic algorithm. In order to design compact light weight HX with the minimum pressure loss and the maximum heat exchange rate, the weight of HX core is chosen as the object function. Dimensions and tube pitch ratio of a HX are used as design variables. Demanded performance such as the pressure loss (${\Delta}P$) and the temperature drop (${\Delta}T$) are used as constraints. The performance of HX is discussed and their optimal designs are presented with an investigation of the effect of design variables and constraints.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.10
• /
• pp.5078-5094
• /
• 2019

In recent years, the development of drone technologies has promoted the widespread commercial application of drones. However, the ability of drone to carry explosives and other destructive materials may bring serious threats to public safety. In order to reduce these threats from illegal drones, acoustic feature extraction and classification technologies are introduced for drone sound identification. In this paper, we introduce the acoustic feature vector extraction method of harmonic line association (HLA), and subband power feature extraction based on wavelet packet transform (WPT). We propose a feature vector extraction method based on combined HLA and WPT to extract more sophisticated characteristics of sound. Moreover, to identify drone sounds, support vector machine (SVM) classification with the optimized parameter by genetic algorithm (GA) is employed based on the extracted feature vector. Four drones' sounds and other kinds of sounds existing in outdoor environment are used to evaluate the performance of the proposed method. The experimental results show that with the proposed method, identification probability can achieve up to 100 % in trials, and robustness against noise is also significantly improved.