• Journal of Electrochemical Science and Technology
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• 제7권2호
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• pp.146-152
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• 2016

A combined system with PEMFC and reformer is introduced and optimized for the real use of this kind of system in the future. The hydrogen source to operate the PEMFC system is methanol, which needs two parts of methanol reforming reaction and preferential oxidation (PROX) for the hydrogen fuel process in the combined operation PEMFC system. With the optimized methanol steam reforming condition, we tested PROX reactions in various operation temperature from 170 to 270 ℃ to investigate CO concentration data in the reformed gases. Using these different CO concentration, PEMFC performances are achieved at the combined system. Pt/C and Ru promoted Pt/C were catalysts were used for the anode to compare the stability in CO contained gases. The alloy catalyst of PtRu/C shows higher performance and better resistance to CO than the Pt/C at even high CO amount of 200 ppm, indicating a promotion not only to the activity but also to the CO tolerance. Furthermore, in a system point of view, there is a fluctuation in the PEMFC operation due to the unstable fuel supply. Therefore, we also modified the methanol reforming by a scaled up reactor and pressurization to produce steady operation of PEMFC. The optimized system with the methanol reformer and PEMFC shows a stable performance for a long time, which is providing a valuable data for the PEMFC commercialization.

• 한국기계가공학회지
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• 제16권5호
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• pp.91-98
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• 2017

This study was conducted to optimize the spring constant and the damping coefficient, which are design parameters of the tractor cabin suspension system, to minimize the ride vibration. A 3D tractor MBD (multi-body dynamics) model with a cabin suspension system was developed using a dynamic analysis program (Recurdyn). Using the developed model and optimization algorithm, the spring constant and the damping coefficient, which are the design parameters of the cabin suspension for the tractor, was were optimized so thatto minimize the maximum overshoot for the vertical displacement of the cabin was minimized. The percent maximum overshoot of the tractor cabin was simulated for the 13 initial models, which were obtained using the ISCD-II method, and for the 3 additional SAO models presented in the optimization algorithm software. The model that represents with the smallest percent maximum overshoot among the 16 models was selected as the optimized model. The percent maximum overshoot of the optimized model was about approximately 5% lower than that of the existing model.

• JSTS:Journal of Semiconductor Technology and Science
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• 제12권2호
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• pp.186-192
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• 2012

Half-rail stacked drivers are used to reduce power consumption of the drivers for synchronous buck converters. In this paper, the stacked driver is optimized by matching the average charging and discharging currents used by high-side and low-side drivers. By matching the two currents, the average intermediate bias voltage can remain constant without the aid of the voltage regulator as long as the voltage ripple stays within the window defined by the hysteresis of the regulator. Thus the optimized driver in this paper can minimize the power consumption in the regulator. The current matching requirement yields the value for the intermediate bias voltage, which deviates from the half-rail voltage. Furthermore the required capacitance is also reduced in this design due to decreased charging current, which results in significantly reduced die area. The detailed analysis and design of the stacked driver is verified through simulations done using 5V MOSFET parameters of a typical 0.35-${\mu}m$ CMOS process. The difference in power loss between the conventional half-rail driver and the proposed driver is less than 1%. But the conventional half-rail driver has excess charge stored in the capacitor, which will be dissipated in the regulator unless reused by an external circuit. Due to the reduction in the required capacitance, the estimated saving in chip area is approximately 18.5% compared to the half-rail driver.

• Smart Structures and Systems
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• 제18권6호
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• pp.1233-1250
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• 2016

Cracks in plate-like structures are some of the main reasons for destruction of the entire structure. In this study, a novel two-stage methodology is proposed for damage detection of flexural plates using an optimized artificial neural network. In the first stage, location of damages in plates is investigated using curvature-moment and curvature-moment derivative concepts. After detecting the damaged areas, the equations for damage severity detection are solved via Bat Algorithm (BA). In the second stage, in order to efficiently reduce the computational cost of model updating during the optimization process of damage severity detection, multiple damage location assurance criterion index based on the frequency change vector of structures are evaluated using properly trained cascade feed-forward neural network (CFNN) as a surrogate model. In order to achieve the most generalized neural network as a surrogate model, its structure is optimized using binary version of BA. To validate this proposed solution method, two examples are presented. The results indicate that after determining the damage location based on curvature-moment derivative concept, the proposed solution method for damage severity detection leads to significant reduction of computational time compared with direct finite element method. Furthermore, integrating BA with the efficient approximation mechanism of finite element model, maintains the acceptable accuracy of damage severity detection.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2005년도 추계학술대회 학술발표 논문집 제15권 제2호
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• pp.483-487
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• 2005

We introduce a new architecture of hetero-hybridized feed-forward neural networks composed of fuzzy set-based polynomial neural networks (FSPNN) and polynomial neural networks (PM) that are based on a genetically optimized multi-layer perceptron and develop their comprehensive design methodology involving mechanisms of genetic optimization and Information Granulation. The construction of Information Granulation based HFSPNN (IG-HFSPNN) exploits fundamental technologies of Computational Intelligence(Cl), namely fuzzy sets, neural networks, and genetic algorithms(GAs) and Information Granulation. The architecture of the resulting genetically optimized Information Granulation based HFSPNN (namely IG-gHFSPNN) results from a synergistic usage of the hybrid system generated by combining new fuzzy set based polynomial neurons (FPNs)-based Fuzzy Neural Networks(PM) with polynomial neurons (PNs)-based Polynomial Neural Networks(PM). The design of the conventional genetically optimized HFPNN exploits the extended Group Method of Data Handling(GMDH) with some essential parameters of the network being tuned by using Genetie Algorithms throughout the overall development process. However, the new proposed IG-HFSPNN adopts a new method called as Information Granulation to deal with Information Granules which are included in the real system, and a new type of fuzzy polynomial neuron called as fuzzy set based polynomial neuron. The performance of the IG-gHFPNN is quantified through experimentation.

• 한국생산제조학회지
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• 제26권4호
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• pp.351-356
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• 2017

The resistance of printed patterns for electrodes fabricated using printing technology should be minimized. This parameter depends on the pattern width and thickness; however, from the viewpoint of printability, the printed patterns should be printed at the designed width. The resistance of the printed patterns as well as printability is affected by various printing conditions. In this paper, the printing condition is optimized to minimize the resistance of electrodes printed by the roll-to-roll gravure method. This is done by considering the spread ratio of pattern width as a parameter of printability using design experiments. The drying temperature, dryer fan speed, and printing speed are selected as effective factors for the experiment objective. The optimized conditions are obtained and reproducibility test using these demonstrates that the optimized conditions can produce low-resistance electrodes for printability of the pattern width.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제1권1호
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• pp.87-94
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• 2001

Silicon nitride films grown by plasma-enhanced chemical vapor deposition (PECVD) are useful for a variety of applications, including anti-reflecting coatings in solar cells, passivation layers, dielectric layers in metal/insulator structures, and diffusion masks. PECVD systems are controlled by many operating variables, including RF power, pressure, gas flow rate, reactant composition, and substrate temperature. The wide variety of processing conditions, as well as the complex nature of particle dynamics within a plasma, makes tailoring SiN film properties very challenging, since it is difficult to determine the exact relationship between desired film properties and controllable deposition conditions. In this study, SiN PECVD modeling using optimized neural networks has been investigated. The deposition of SiN was characterized via a central composite experimental design, and data from this experiment was used to train and optimize feed-forward neural networks using the back-propagation algorithm. From these neural process models, the effect of deposition conditions on film properties has been studied. A recipe synthesis (optimization) procedure was then performed using the optimized neural network models to generate the necessary deposition conditions to obtain several novel film qualities including high charge density and long lifetime. This optimization procedure utilized genetic algorithms, hybrid combinations of genetic algorithm and Powells algorithm, and hybrid combinations of genetic algorithm and simplex algorithm. Recipes predicted by these techniques were verified by experiment, and the performance of each optimization method are compared. It was found that the hybrid combinations of genetic algorithm and simplex algorithm generated recipes produced films of superior quality.

• International Journal of Fluid Machinery and Systems
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• 제5권3호
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• pp.100-108
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• 2012

Design parameters for suction channels of process centrifugal compressors were investigated, and an optimization method to enhance stage efficiency by using the new design parameters was proposed. From results of computational fluid dynamics, the passage sectional area ratios $A_c/A_e$, $A_e/A_s$ and $A_c/A_s$ were found to be the dominant parameters for the pressure loss and circumferential flow distortion, where $A_c$, $A_e$ and $A_s$ are passage sectional areas for the casing upstream side, casing entrance and impeller eye, respectively. The Base suction channel was optimized using the new design parameters, and the Base and Optimized types were tested. Test results showed that the Optimized suction channel achieved 3.8% higher stage efficiency than the Base suction channel while maintaining the same operating range.

• 제어로봇시스템학회논문지
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• 제20권9호
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• pp.936-941
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• 2014

Walking mechanisms are very important for legged robots to ensure their stable locomotion. In this research, Klann-linkage is suggested as a walking mechanism for a water-running robot and is optimized using level average analysis. The structure of the Klann-linkage is introduced first and design variables for the Klann-linkage are identified considering the kinematic task of the walking mechanism. Next, the design problem is formulated as a path generation optimization problem. Specifically, the desired path for the foot-pad is defined and the objective function is defined as the structural error between the desired and the generated paths. A process for solving the optimization problem is suggested utilizing the sensitivity analysis of the design variables. As a result, optimized lengths of Klann-linkage are obtained and the optimum trajectory is obtained. It is found that the optimized trajectory improves the cost function by about 62% from the initial one. It is expected that the results from this research can be used as a good example for designing legged robots.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1997년도 추계학술대회 논문집
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• pp.454-457
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• 1997

The purpose of this study is to develop an optimal model, using existing models, that is able to estimate the amount of spatter utilizing artificial neural network in the short circuit transfer mode of gas metal arc (GMA) welding. The amount of spatter generated during welding can become a barometer which represents the process stability of metal transfer in GMA welding, and it depends on some factors which constitute a periodic waveforms of welding current and arc voltage in short circuit GMA welding. So, the 12 factors, which could express the characteristics for the waveforms, and the amount of spatter are used as input and output variables of the neural network, respectively. Two neural network models to estimate the amount of spatter are proposed: A neural network model, where arc extinction is not considered, and a combined neural network model where it is considered. In order to reduce the calculation time it take to produce an output, the input vector and hidden layers for each model are optimized using the correlation coefficients between each factor and the amount of spattcr. The est~mation performance of each optimized model to the amount of spatter IS assessed and compared to the est~mation performance of the model proposed by Kang. Also, through the evaluation for the estimation performance of each optimized model, it is shown that the combined neural network model can almost perfectly predict the amount of spatter.