• Transactions of the Korean Society of Mechanical Engineers B
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• v.39 no.2
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• pp.161-168
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• 2015

An experimental study was conducted to scrutinize the influence of the frequency of an ultrasonic standing wave on the variation in the behavior of a methane-air premixed flame. The evolutionary features of the propagating flame were captured by a high-speed camera, and the macroscopic flame behavior, including the flame structure and local velocities, was investigated in detail using a post-processing analysis of the high-speed images. It was found that a structural variation and propagation-velocity augmentation of the methane-air premixed flame were caused by the intervention of the ultrasonic standing wave, which enhanced the combustion reaction. Conclusive evidence for the dependency of the flame behaviors on the driving frequency of the ultrasonic standing wave and equivalence ratio of the reactants is presented.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.16 no.2
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• pp.7-15
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• 2017

Recently, an electricity sensor system has been installed and operated to prevent failures and accidents by identifying whether a transformer is normal in advance of failure. This electricity sensor system is able to both measure and monitor the transformer's power and voltage remotely and send information to a manager when unusual operation is discovered. However, a battery is required to operate power detection devices, and battery systems need ongoing management such as regular replacement. In addition, at a maintenance cost, occasional human resources and worker safety problems arise. Accordingly, we apply a linear electromagnetic generator using vibration energy from a transformer for an electric sensor system's drive in this research and we conduct optimal design to maximize the linear electromagnetic generator's power. We consider design variables using the provided design method from Process Integration, Automation, and Optimization (PIAnO), which is common tool from process integration and design optimization (PIDO). In addition, we analyze the experiment point from the design of the experiments using "MAXWELL," which is a common electromagnet analysis program. We then create an approximate model and conduct accuracy verification. Finally, we determine the optimal model that generates the maximum power using the proven approximate kriging model and evolutionary optimization algorithm, which we then confirm via simulation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.8
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• pp.19-24
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• 2018

The IP Multimedia Subsystem(IMS) is an architectural framework for delivering IP multimedia services to mobile users. In order to guarantee the reliability and Quality of Service(QoS) of a variety of multimedia services, we need a new evolutionary approach that maintains the IMS based signaling platform which can perform the processing of flow through distributed controllers. Software Defined Network(SDN) is an architecture purporting to be distributed, dynamic, cost-effectives as well as adapting and seeking to be suitable for the high-bandwidth, dynamic nature of today's applications. It requires some methods for the control plane to communication with the data plane. One of such mechanisms is OpenFlow which is a prominent standard protocol and interface that is responsible for managing the network resources by using the remote SDN controller. In this paper, we propose a straightforward approach for integrating SDN technology together with the IMS architecture. Therefore we propose and construct a combined architecture model that performs flow processing using OpenFlow via the IMS based signaling platform, which maintains the existing telecom call service. Additionally, we describe some relevant experimentation results from the proposed architecture.

• Journal of Microbiology and Biotechnology
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• v.25 no.10
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• pp.1599-1605
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• 2015

The development of rapid and efficient genome sequencing methods has enabled us to study the evolutionary background of bacterial genetic information. Here, we present comparative genomic analysis of 17 Streptomyces species, for which the genome has been completely sequenced, using the pan-genome approach. The analysis revealed that 34,592 ortholog clusters constituted the pan-genome of these Streptomyces species, including 2,018 in the core genome, 11,743 in the dispensable genome, and 20,831 in the unique genome. The core genome was converged to a smaller number of genes than reported previously, with 3,096 gene families. Functional enrichment analysis showed that genes involved in transcription were most abundant in the Streptomyces pan-genome. Finally, we investigated core genes for the sigma factors, mycothiol biosynthesis pathway, and secondary metabolism pathways; our data showed that many genes involved in stress response and morphological differentiation were commonly expressed in Streptomyces species. Elucidation of the core genome offers a basis for understanding the functional evolution of Streptomyces species and provides insights into target selection for the construction of industrial strains.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.34 no.7
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• pp.679-688
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• 2010

The major objective of the present study is to extend the applications of inverse analysis to more realistic engineering fields with a complex combustion process rather than the traditional simple heat-transfer problems. In order to do this, the unknown initial mass fractions of $CH_4/O_2$ are estimated from the temperature measurement data by inverse analysis in the practical diffusion-controlled turbulent combustion problem. In order to ensure efficient inverse analysis, the repulsive particle swarm optimization (RPSO) method, which belongs to the class of stochastic evolutionary global optimization methods, is implemented as an inverse solver. Based on this study, it is expected that useful information can be obtained when inverse analysis is used in the diagnosis, design, or optimization of real combustion systems involving unknown parameters.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.395-395
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• 2000

Optimization of existing processes becomes more important than the past as environmental problems and concerns about energy savings stand out. When we can model a process mathematically, we can easily optimize it by using the model as constraints. However, modeling is very difficult for most chemical processes as they include numerous units together with their correlation and we can hardly obtain parameters. Therefore, optimization that is based on the process models is, in turn, hard to perform. Especially, f3r unknown processes, such as bioprocess or microelectronics materials process, optimization using mathematical model (first principle model) is nearly impossible, as we cannot understand the inside mechanism. Consequently, we propose a few optimization method using empirical model evolutionarily instead of mathematical model. In this method, firstly, designing experiments is executed fur removing unecessary experiments. D-optimal DOE is the most developed one among DOEs. It calculates design points so as to minimize the parameters variances of empirical model. Experiments must be performed in order to see the causation between input variables and output variables as only correlation structure can be detected in historical data. And then, using data generated by experiments, empirical model, i.e. response surface is built by PLS or MLR. Now, as process model is constructed, it is used as objective function for optimization. As the optimum point is a local one. above procedures are repeated while moving to a new experiment region fur finding the global optimum point. As a result of application to the pulp digester benchmark model, kappa number that is an indication fur impurity contents decreased to very low value, 3.0394 from 29.7091. From the result, we can see that the proposed methodology has sufficient good performance fur optimization, and is also applicable to real processes.

• Korean Journal of Construction Engineering and Management
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• v.8 no.5
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• pp.119-131
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• 2007

Design build (DB) is a construction project delivery system which has become one of the favored ones and the growth of DB by the public agencies has been steadily increasing. DB can be viewed as an evolutionary project delivery system, since DB project team members collaborate on work with each other, keep seeking cost effective and innovative alternatives that meet the construction needs of the project. Thus DB outperformed others in delivery speed, cost saving, and turnover quality. In Korea, DB projects likewise have steadily increased that it is needed to analyze characteristics of DB delivery system in Korea. For that purpose, this research would offer an analysis of characteristics of DB delivery system in Korea using system dynamics model with investigating the public market trend, comparing DB performance to DBB, and analyzing questionnaire survey.

• Korean Chemical Engineering Research
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• v.52 no.1
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• pp.8-16
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• 2014

Microalgae have been considered as a promising microorganism in the field of bio-industry due to their abilities to fix carbon dioxide as well as biosynthesize valuable secondary metabolites. Of many lighting sources for microalgal cultivation, LED (Lighting Emitting Diode) has been emerged as the appropriate choice with multiple advantages over the conventional bulbs. However, it is only in recent years that we have witnessed the possibility of application of LED into microalgae cultivation system. LED will serve as an evolutionary lighting source for microalgae cultivation system and open the frontier for integrative bio-industries. In this paper, we present the comprehensive review on the recent trends of LED applications into microalgal biotechnology.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.5
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• pp.477-482
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• 2015

Linear regressions and evolutionary nonlinear regression based compensation techniques for the short-range prediction of wind speed are investigated. Development of an efficient MOS(Model Output Statistics) is necessary to correct systematic errors of the model, but a linear regression based MOS is hard to manage an irregular nature of weather prediction. In order to solve the problem, a nonlinear and symbolic regression method using GP(Genetic Programming) is suggested for a development of MOS for wind speed prediction. The proposed method is compared to various linear regression methods for prediction of wind speed. Also, statistical analysis of distribution for UM elements for each method is executed. experiments are performed for KLAPS(Korea Local Analysis and Prediction System) re-analysis data from 2007 to 2013 year for Jeju Island and Busan area in South Korea.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.1
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• pp.26-32
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• 2018

The protein secondary structures are important information for studying the evolution, structure and function of proteins. Recently, deep learning methods have been actively applied to predict the secondary structure of proteins using only protein sequence information. In these methods, widely used input features are protein profiles transformed from protein sequences. In this paper, to obtain an effective protein profiles, protein profiles were constructed using protein sequence search methods such as PSI-BLAST and HHblits. We adjust the similarity threshold for determining the homologous protein sequence used in constructing the protein profile and the number of iterations of the profile construction using the homologous sequence information. We used the protein profiles as inputs to convolutional neural networks and recurrent neural networks to predict the secondary structures. The protein profile that was created by adding evolutionary information only once was effective.