• Journal of Welding and Joining
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• v.23 no.6
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• pp.77-86
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• 2005

Low temperature bonding of the silicon and glass using the Spin-on Glass (SOG) has been conducted experimentally to figure out the effects of the SOG solution composition and process variables on bond strength using the Design of Experiment method. In order to achieve the high quality bond interface without rack, sufficient reaction time of the optimal SOG solution composition is needed along with proper pressure and annealing temperature. The shear strength under the optimal SOG solution composition and process condition was higher than that of conventional anodic bonding and similar to that of wafer direct bonding.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.43 no.1
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• pp.1-5
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• 1998

Experiments were carried out to find out the optimum water potential, temperature, and duration for the priming of rice seeds, Oryza sativa L. (cv. Ilpumbyeo) for better germination at sub-optimal temperatures. Seeds were primed in 0, -0.2, -0.4, -0.6, -0.8, and -1.0 MPa PEG (polyethylene glycol) solutions at $25^{\circ}C$. The optimum water potential for seed priming, the highest water potential at which rice seeds did not germinate, was -0.6 MPa. To find out optimum priming temperature and duration rice seeds were primed in -0.6 MPa PEG solution and 0 MPa (water as a control) for various durations at 15 and $25^{\circ}C$ and the seeds were germinated at 17, 20, and $25^{\circ}C$. Considering germination rate and speed, the optimum priming time in water (0 MPa) was 4 days at 15$^{\circ}C$ and 1 day at $25^{\circ}C$, while 4 days was the optimum priming time in a -0.6 MPa PEG solution, regardless of the priming temperature. Priming reduced the actual time of germination, especially at sub-optimal temperatures. Priming did not affect germination rate in -0.6 MPa PEG solution at 15$^{\circ}C$, but overpriming reduced the final germination rate in water at 15$^{\circ}C$ and in -0.6 PEG solution at $25^{\circ}C$. Total sugars and $\alpha$-amylase activity induced during the seed priming were negatively correlated with the final germination rate and there was no noted relationship with the speed or uniformity of germination.

• Journal of IKEEE
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• v.13 no.3
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• pp.24-31
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• 2009

The computation time of FEA(finite element analysis) for one model may range from a few seconds up to several hours according to the complexity of the simulated model. If these FEA is used to calculate the objective and the constraint functions during the optimal solution search, it causes very excessive execution time. To resolve this problem, the distributed computing technique using internet web service is proposed in this paper. And the dynamic load balancing mechanisms are established to advance the performance of distributed computing. To verify its validity, this method is applied to a traditional mathematical optimization problem. And the proposed FEA-based optimization using internet distributed computing is applied to the optimal design of the permanent magnet dc motor(PMDCM) for automotive application.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.12
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• pp.2687-2696
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• 2011

A Clustering problem is one of the organizational problems to improve network lifetime and scalability in underwater acoustic sensor networks. This paper propose an algorithm to obtain an optimal clustering solution to be able to minimize a total transmission power for all deployed nodes to transmit data to the sink node through its clusterhead. In general, as the number of nodes increases, the amount of calculation for finding the solution would be too much increased. To obtain the optimal solution within a reasonable computation time, we propose a genetic algorithm to obtain the optimal solution of the cluster configuration. In order to make a search more efficient, we propose some efficient neighborhood generating operations of the genetic algorithm. We evaluate those performances through some experiments in terms of the total transmission power of nodes and the execution time of the proposed algorithm. The evaluation results show that the proposed algorithm is efficient for the cluster configuration in underwater acoustic sensor networks.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.203-210
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• 2017

Data clustering is one of the most difficult and challenging problems and can be formally considered as a particular kind of NP-hard grouping problems. The K-means algorithm is one of the most popular and widely used clustering method because it is easy to implement and very efficient. However, it has high possibility to trap in local optimum and high variation of solutions with different initials for the large data set. Therefore, we need study efficient computational intelligence method to find the global optimal solution in data clustering problem within limited computational time. The objective of this paper is to propose a combined artificial bee colony (CABC) with K-means for initialization and finalization to find optimal solution that is effective on data clustering optimization problem. The artificial bee colony (ABC) is an algorithm motivated by the intelligent behavior exhibited by honeybees when searching for food. The performance of ABC is better than or similar to other population-based algorithms with the added advantage of employing fewer control parameters. Our proposed CABC method is able to provide near optimal solution within reasonable time to balance the converged and diversified searches. In this paper, the experiment and analysis of clustering problems demonstrate that CABC is a competitive approach comparing to previous partitioning approaches in satisfactory results with respect to solution quality. We validate the performance of CABC using Iris, Wine, Glass, Vowel, and Cloud UCI machine learning repository datasets comparing to previous studies by experiment and analysis. Our proposed KABCK (K-means+ABC+K-means) is better than ABCK (ABC+K-means), KABC (K-means+ABC), ABC, and K-means in our simulations.

• Journal of the Korea Society of Computer and Information
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• v.20 no.1
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• pp.111-118
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• 2015

This paper deals with the routing and wavelength assignment problem (RWAP) that decides the best lightpaths for multiple packet demands for (s,t) in optical communication and assigns the minimum number of wavelengths to given lightpaths. There has been unknown of polynomial-time algorithm to obtain the optimal solution for RWAP. Hence, the RWAP is classified as NP-complete problem and one can obtain the approximate solution in polynomial-time. This paper decides the shortest main and alternate lightpath with same hop count for all (s,t) for given network in advance. When the actual demands of communication for particular multiple packet for (s,t), we decrease the maximum utilized edge into b utilized number using these dual-paths. Then, we put these (s,t) into b-wavelength bins without duplicated edge. This algorithm can be get the optimal solution within O(kn) computational complexity. For two experimental data, the proposed algorithm shows that can be obtain the known optimal solution.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.6
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• pp.47-53
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• 2010

The paper presents a method for decision the optimal cycle time during the vehicle traffic control in single intersection with AHP. To solve this uncertainty optimization problem, the optimization index in the form of linear addition and fuzzy measurement is assumed and fuzzy integral is used. Examples of solution for two cases of optimal cycle time in two traffic controller are presented and compared.

• Journal of the Korean Operations Research and Management Science Society
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• v.10 no.2
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• pp.54-64
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• 1985

Biomass conversion processes have the potential for satisfying approximately 25% of the national demand for methane gas. At the current time very littel analytical work has been done to optimally design and operate the production facilities associated with these processes. This study was motivated by the high cost of these proposed systems. The biomass in storage decays (exponentially) with time while the batch methane production rate decreases (exponentially) over time. The basic problem is to determine the optimal residence times for batches in the anaerobic degester to maximize total production over a fixed planning horizon. The analysis characteries the form of the optimal policy and presents efficient algorithm for obtaining this solution.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.7
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• pp.76-82
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• 2004

The solutions of neighboring optimal control are typically obtained using the sweep method or transition matrices. Due to the numerical integration, however, the gain matrix can become infinite as time go to final one in the transition matrices, and the Riccati solution can become infinite when the final time free. To overcome these disadvantages, this paper proposes the pseudospectral Legendre method which is to first discreteize the linear boundary value problem using the global orthogonal polynomial, then transforms into an algebraic equations. Because this method is not necessary to take any integration of transition matrix or Riccati equation, it can be usefully used in real-time operation. Finally, its performance is verified by the numerical example for the space vehicle's orbit transfer.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.10
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• pp.1699-1708
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• 2007

The optimal power flow(OPF) problem was introduced by Carpentier in 1962 as a network constrained economic dispatch problem. Since then, the OPF problem has been intensively studied and widely used in power system operation and planning. In these days, OPF is becoming more and more important in the deregulation environment of power pool and there is an urgent need of faster solution technique for on-line application. To solve OPF problem, many heuristic optimization methods have been developed, such as Genetic Algorithm(GA), Evolutionary Programming(EP), Evolution Strategies(ES), and Particle Swarm Optimization(PSO). Especially, PSO algorithm is a newly proposed population based heuristic optimization algorithm which was inspired by the social behaviors of animals. However, population based heuristic optimization methods require higher computing time to find optimal point. This shortcoming is overcome by a straightforward parallel processing of PSO algorithm. The developed parallel PSO algorithm is implemented on a PC cluster system with 6 Intel Pentium IV 2GHz processors. The proposed approach has been tested on the IEEE 30-bus system. The results showed that computing time of parallelized PSO algorithm can be reduced by parallel processing without losing the quality of solution.