• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.5
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• pp.487-496
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• 2000

In this paper, the Multi-FNN(Fuzzy-Neural Networks) model is identified and optimized using HCM(Hard C-Means) clustering method and genetic algorithms. The proposed Multi-FNN is based on Yamakawa's FNN and uses simplified inference as fuzzy inference method and error back propagation algorithm as learning rules. We use a HCM clustering and Genetic Algorithms(GAs) to identify both the structure and the parameters of a Multi-FNN model. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNN according to the divisions of input-output space using I/O process data. Also, the parameters of Multi-FNN model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. A aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. The aggregate performance index stands for an aggregate objective function with a weighting factor to consider a mutual balance and dependency between approximation and predictive abilities. According to the selection and adjustment of a weighting factor of this aggregate abjective function which depends on the number of data and a certain degree of nonlinearity, we show that it is available and effective to design an optimal Multi-FNN model. To evaluate the performance of the proposed model, we use the time series data for gas furnace and the numerical data of nonlinear function.

• Journal of the Earthquake Engineering Society of Korea
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• v.6 no.4
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• pp.15-22
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• 2002

In the course of seismic probabilistic risk assessment(SPRA), seismic fragility analysis(SFA) is utilized as a tool to evaluate the actual seismic capacity of structures. This paper introduces a methodology of SFA and its evaluation procedures, especially focusing on the basic fragility variables. A new definition of the response spectrum shape factor as one of the most critical basic variables is suggested. The new factor is expressed as a term of linear algebraic sum using the modal contribution factor. The efficiency of new response spectrum shape factor is evaluated and validated to use in practice through the case study of the nuclear power plant structures. The case study results show that the proposed method can be effectively applicable to multi-mode structures with composite modal damping.

• The KSFM Journal of Fluid Machinery
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• v.16 no.1
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• pp.32-39
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• 2013

The multi-stage control valve is one of the devices which controls cavitation and high pressure drop. To attain the high pressure drop, the conventional control valves adopted the multi-stage trim to avoid the occurrence of local cavitation in valves. This work studied the effect of divergence angle on the characteristics of multi-stage trim. Pressure drop and flow characteristics was calculated for the 1 passage of multi-staged trim by using the FLUENT 6.3.26. The result showed that the pressure drop is significantly influenced by the divergence angle of multi-stage trim. In addition, the pressure drop increased consistently as the Reynolds number and divergence angle increases.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.26 no.1
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• pp.30-35
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• 2017

The major parts of a vacuum cleaner are molded. The vacuum cleaner works in multi-load conditions. Therefore, the designer needs to optimize the structure and injection molding conditions simultaneously. Here, the main factor of design is the rib shape and thickness. The greater the rib thickness, the greater the stiffness of the structure. However, it causes an increase in weight. On the other hand, the lower the rib thickness, the greater the increase in the injection pressure. However, the weight will be reduced. Therefore, the designer needs to optimize the rib shape and thickness for structure stiffness and injection molding. In order to solve this problem, we propose an optimization method using D.O.E and a response surface model, which is a multi-objective optimization method using the multi-objective genetic algorithm.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.12 no.1
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• pp.45-53
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• 2006

Thermal insulation is used in a variety of applications to protect temperature sensitive products from thermal damage. Several factors affect the performance of insulation packages. Among these factors, the thermal resistance of the insulating wall is the most important factor to determine the performance of the insulating package. In many cases, insulating wall consists of multi-layered structure and the heat transfer through this structure is a very complex process. In this study, an one-dimensional mathematical model, which includes all of the heat transfer principles covering conduction, convection and radiation in multi-layered structure, were developed. Based on this model, several heat transfer phenomena occurred in the air space between the layer of the insulating wall were investigated. From the simulation results, it was observed that the heat transfer through the air space between the layer were dominated by conduction and radiation and the low emissivity of the surface of each solid layer of the wall can dramatically increase the thermal resistance of the wall. For practical use, an equation was derived for the calculation of the thermal resistance of a multi-layered wall.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.420-423
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• 2001

In this paper, the Multi-FNNs(Fuzzy-Neural Networks) architecture is identified and optimized using HCM(Hard C-Means) clustering method and genetic algorithms. The proposed Multi-FNNs architecture uses simplified inference and linear inference as fuzzy inference method and error back propagation algorithm as learning rules. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNNs according to the divisions of input-output space using I/O process data. Also, the parameters of Multi-FNNs model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. To evaluate the performance of the proposed model we use the time series data for gas furnace and the NOx emission process data of gas turbine power plant.

• Proceedings of the IEEK Conference
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• 2001.06a
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• pp.269-272
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• 2001

Since Multi-way Search Tree reduces the number of the memory access by increasing the branch factor, it is considered a method to archive the high-speed IP address lookup. Using the combination of initial 16 bit may and Multi-way Search Tree, it also reduces the search time of If address lookup. Multi-way Search Tree consists of K keys and K＋1 key pointers. This paper shows how the E update of Multi-way Search Tree which consists of the one pointer within a node can be performed. Using the one pointer within a node, it increases the number of keys within a node and reduces the search time of IP lookup. We also describes IP updating methods such as modification, Insertion and Deletion of address entries. Our update scheme performs better than the method which rebuilds the entire IP routing table when IP update processes.

• Journal of Magnetics
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• v.15 no.1
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• pp.36-39
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• 2010

The multi-degrees of freedom surface permanent-magnet motor (Multi-D.O.F. SPM) has several degrees of freedom operations that are defined as the "roll", "yaw", and "pitch". Normally, the torque that is generated to rotate a rotor includes ripples. The analysis of the torque ripples is important for improving motor performance. In terms of the electric analysis, torque ripple occurs as a result of many factors, including the rotor and stator structures, the distribution of the air-gap flux density, and the waveform of the current in the coils. In particular, the torque ripple is an important factor in the stable operation of the Multi-D.O.F. SPM. Therefore, in this work, the torque ripple was analyzed using various types of magnetization for the permanent magnet. An improved model was proposed for the Multi-D.O.F. SPM based on this analysis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.8
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• pp.3823-3840
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• 2017

This paper analyzes the nodes deployment optimization problem in energy constrained wireless sensor networks, which multi-hop cooperative beamforming (CB) based cooperative-multi-input-single-output (CMISO) transmission is adopted to reduce the energy consumption. Firstly, we establish the energy consumption models for multi-hop SISO, multi-hop DSTBC based CMISO, multi-hop CB based CMISO transmissions under random nodes deployment. Then, we minimize the energy consumption by searching the optimal nodes deployment for the three transmissions. Furthermore, numerical results present the optimal nodes deployment parameters for the three transmissions. Energy consumption of the three transmissions are compared under optimal nodes deployment, which shows that CB based CMISO transmission consumes less energy than SISO and DSTBC based CMISO transmissions. Meanwhile, under optimal nodes deployment, the superiorities of CB based CMISO transmission over SISO and DSTBC based CMISO transmissions can be more obvious when path-loss-factor becomes low.

• Journal of Sensor Science and Technology
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• v.30 no.4
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• pp.218-222
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• 2021

Wearable multi-sensors based on nanocrystals have attracted significant attention, and studies on patterning technology to implement such multi-sensors are underway. Conventional patterning processes may affect material properties based on high temperatures and harsh chemical conditions. In this study, we developed an inkjet printing technique that can overcome these drawbacks through the application of patterning processes at room temperature and atmospheric pressure. Nanocrystal-based ink is used to adjust properties efficiently. Additionally, the viscosity and surface tension of the solvents are investigated and optimized to increase patterning performance. In the patterning process, the electrical, electrothermal, and electromechanical properties of the nanocrystal pattern are controlled by the ligand exchange process. Experimental results demonstrate that a multi-sensor with a temperature coefficient of resistance of 3.82 × 10^-3 K^-1 and gauge factor of 30.6 can be successfully fabricated using one-step inkjet printing.