• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.43 no.3
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• pp.1-10
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• 2011

Proportional.Integral.Derivative control schemes continue to provide the simplest and effective solutions to most of the control engineering applications today. However PID controller is poorly tuned in practice with most of the tuning done manually which is difficult and time consuming. This research comes up with a soft computing approach involving Genetic Algorithm, Evolutionary Programming, and Particle Swarm Optimization and Ant colony optimization. The proposed algorithm is used to tune the PID parameters and its performance has been compared with the conventional methods like Ziegler Nichols and Lambda method. The results obtained reflect that use of heuristic algorithm based controller improves the performance of process in terms of time domain specifications, set point tracking, and regulatory changes and also provides an optimum stability. This research addresses comparison of tuning of the PID controller using soft computing techniques on Machine Direction of basics weight control in pulp and paper industry. Compared to other conventional PID tuning methods, the result shows that better performance can be achieved with the soft computing based tuning method. The ability of the designed controller, in terms of tracking set point, is also compared and simulation results are shown.

• Journal of Communications and Networks
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• v.15 no.3
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• pp.266-275
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• 2013

The exact closed-form expressions for outage probability and bit error rate of spectrum sharing-based multi-hop decode-and-forward (DF) relay networks in non-identical Rayleigh fading channels are derived. We also provide the approximate closed-form expression for the system ergodic capacity. Utilizing these tractable analytical formulas, we can study the impact of key network parameters on the performance of cognitive multi-hop relay networks under interference constraints. Using a linear network model, we derive an optimum relay position scheme by numerically solving an optimization problem of balancing average signal-to-noise ratio (SNR) of each hop. The numerical results show that the optimal scheme leads to SNR performance gains of more than 1 dB. All the analytical expressions are verified by Monte-Carlo simulations confirming the advantage of multihop DF relaying networks in cognitive environments.

• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.577-582
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• 2022

For developing a new embedded system, an application program/an emulator and a compiler are developed simultaneously. In order to provide the optimal performance of all system components, local optimization should be carried out for the developing process. For this purpose, if a source-level performance analyzer is developed, it is possible to optimize the application program's source code by the performance evaluation. In general, the performance of an application program is determined in the loop iterations. The Intermediate Representation (IR) code generator generates IR code from the source code, and evaluates the execution time with the instructions in the intermediate representation code. If the source code is improved based on the evaluated result, better results can be obtained in the final application code. This study describes the source-level performance analyzer that can be used during the simultaneous development of the new embedded system and its application programs. The performance analyzer makes it possible to more quickly optimize the performance of the new embedded system.

• The KSFM Journal of Fluid Machinery
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• v.16 no.1
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• pp.11-16
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• 2013

A numerical study was conducted to improve the performance of an impeller of centrifugal compressor. Nine design variables were chosen with constraints. Only meridional contours and blade profile were adjusted. ANN (Artificial Neural Net) was adopted as a main optimization algorithm with PSO (Particle Swarm Optimization) in order to reduce the optimization time. At first, ANN was learned and trained with the design variable sets which were obtained using DOE (Design of Experiment). This ANN was continuously improved its accuracy for each generation of which population was one hundred. New design variable set in each generation was selected using a non-gradient based method of PSO in order to obtain the global optimized result. After $7^{th}$ generation, the prediction difference of efficiency and pressure ratio between ANN and CFD was less than 0.6%. From more than 1,200 design variable sets, a pareto of efficiency versus pressure ratio was obtained and an optimized result was selected based on the multi-objective function. On this optimized impeller, the efficiency and pressure ratio were improved by 1% and 9.3%, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.61-80
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• 2018

Location information of individual nodes is important in the implementation of necessary network functions. While extensive studies focus on localization techniques in 2D space, few approaches have been proposed for 3D positioning, which brings the location closer to the reality with more complex calculation consumptions for high accuracy. In this paper, an effective range-free localization scheme is proposed for 3D space localization, and the sensitivity of parameters is evaluated. Firstly, we present an improved algorithm (MDV-Hop), that the average distance per hop of the anchor nodes is calculated by root-mean-square error (RMSE), and is dynamically corrected in groups with the weighted RMSE based on group hops. For more improvement in accuracy, we expand particle swarm optimization (PSO) of intelligent optimization algorithms to MDV-Hop localization algorithm, called PMDV-hop, in which the parameters (inertia weight and trust coefficient) in PSO are calculated dynamically. Secondly, the effect of various localization parameters affecting the PMDV-hop performance is also present. The simulation results show that PMDV-hop performs better in positioning accuracy with limited energy.

• KIEAE Journal
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• v.16 no.5
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• pp.21-28
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• 2016

Purpose: The Objective of this study is to explore the capabilities of an integrated modelling and simulation workflow when applied to an experiment-based research process, aimed at deriving daylight optimization strategies specific to tall buildings. Methods: Two methods were devised to apply this workflow with the help of DIVA and Rhino/Grasshopper. The first method is a multiple variant analysis by setting up an appropriate base case and analysing its daylight and energy performance, forming the basis of comparison for subsequent cases for design variants. The second method involved setting up the base case within a site context and conducting a solar irradiation study. An architectural variables such as overhang and shading device, were then defined as inputs in the parametric definition in Grasshopper to control the selected variable. Results: While the first method took advantage of the speed and efficiency of the integrated workflow, the second method was derived based on the ability to directly process simulation data within the integrated, single-software platform of the proposed workflow. Through these methods, different architectural strategies were explored, both to increase daylight penetration and to reduce radiant heat gain. The focus is on methods by which this workflow can be applied to facilitate the experimental derivation of daylight optimization strategies that are specific to tall building design.

• Transactions of the Korean Society of Automotive Engineers
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• v.9 no.2
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• pp.116-129
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• 2001

Traditional occupant analysis has been performed with a pre-determined crash puse which is produced from a test and the involved components are designed based on the analysis resuls. The method has limitations in that the design does not have much freedom. Howrver, if a good crash pulse is proposed, the body structure can be modified to generate the crash pulse. Therefore, it is assumed that the crash pulse can be changed to imptove the occupant crash performance. A preferable crash pulse is determined to minimize the occupant injuty. A constraint is established to keep the phenomena of physics valid. The response surface method(RSM) is adopted for the optimization process. An RSM in a commercial code is utilzed by interfacing with an in-house occupant analysis program called SAFE(Safety Analysis For occupant crash Enviroment). Design of involved components called is carried out through optimization with the RSM. The advantages of the RSM are investigated as opposed to other methods, and the tesults are compared. Also, the design under the new crach pulse is compared with that trom the pre-detetmined pulse.

• The Journal of Korea Robotics Society
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• v.8 no.2
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• pp.92-103
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• 2013

Humanoid robot is the most intimate robot platform suitable for human interaction and services. Biped walking is its basic locomotion method, which is performed with combination of joint actuator's rotations in the lower extremity. The present work employs humanoid robot simulator and numerical optimization method to generate optimal joint trajectories for biped walking. The simulator is developed with Matlab based on the robot structure constructed with the Denavit-Hartenberg (DH) convention. Particle swarm optimization method minimizes the cost function for biped walking associated with performance index such as altitude trajectory of clearance foot and stability index concerning zero moment point (ZMP) trajectory. In this paper, instead of checking whether ZMP's position is inside the stable region or not, reference ZMP trajectory is approximately configured with feature points by which piece-wise linear trajectory can be drawn, and difference of reference ZMP and actual one at each sampling time is added to the cost function. The optimized joint trajectories realize three phases of stable gait including initial, periodic, and final steps. For validation of the proposed approach, a small-sized humanoid robot named DARwIn-OP is commanded to walk with the optimized joint trajectories, and the walking result is successful.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.4
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• pp.207-210
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• 2012

Genetic algorithms(GA) are well known and very popular stochastic optimization algorithm. Although, GA is very powerful method to find the global optimum, it has some drawbacks, for example, premature convergence to local optima, slow convergence speed to global optimum. To enhance the performance of GA, this paper proposes an adaptive partitioning-based genetic algorithm. The partitioning method, which enables GA to find a solution very effectively, adaptively divides the search space into promising sub-spaces to reduce the complexity of optimization. This partitioning method is more effective as the complexity of the search space is increasing. The validity of the proposed method is confirmed by applying it to several bench mark test function examples and the optimization of fuzzy controller for the control of an inverted pendulum.

• Journal of Distribution Science
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• v.15 no.6
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• pp.37-46
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• 2017

Purpose - The paper aims to propose an optimization model for supporting the buyer-seller negotiations. We consider the price, quality, and delivery as evaluation criteria, also recognized as objectives for negotiation. Research design, data, and methodology - The methodology used in this paper involves the input-oriented DEA with the inverse optimization. Under the existence of several potential suppliers, the price would be considered to be the decision variable to conclude the negotiation so as to meet the desired level of the quality and delivery. The data set for six suppliers with three criteria is examined by the proposed approach. Results - We present the decision aid model by displaying the price spectrum as the changes of desired output levels. It overcomes the shortcomings from previous researches mainly based on the discrete types of scenario generations. This approach shows that the obtained results help the buyer understand the trade-offs between price and performance when he/she considers the negotiation. Conclusions - The paper contributes to the numerical models for buyer-supplier negotiation in that the model for the supplier evaluation and selection is closely linked with the model for negotiation. In addition, it eliminates the unrealistic negotiation strategy, and provides the negotiation strategies that the buyer would not shift the burden on suppliers by maintaining the current efficiency.