• Industrial Engineering and Management Systems
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• v.13 no.4
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• pp.369-382
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• 2014

This paper introduces a new multi-product multi-period multi-objective aggregate production planning problem. The proposed problem is modeled using multi-objective mixed-integer mathematical programming. Three objective functions, including minimizing total cost, maximizing customer services level, and maximizing the quality of end-product, are considered, simultaneously. Several constraints such as quantity of production, available time, work force levels, inventory levels, backordering levels, machine capacity, warehouse space and available budget are also considered. Some parameters of the proposed model are assumed to be qualitative and modeled using fuzzy sets. Then, a fuzzy goal programming approach is proposed to solve the model. The proposed approach is applied on a real-world industrial case study of a color and resin production company called Teiph-Saipa. The approach is coded using LINGO software. The efficacy and applicability of the proposed approach are illustrated in the case study. The results of proposed approach are compared with those of the existing experimental methods used in the company. The relative dominance of the proposed approach is revealed in comparison with the experimental method. Finally, a data dictionary, including the way of gathering data for running the model, is proposed in order to facilitate the re-implementation of the model for future development and case studies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.4
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• pp.1424-1440
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• 2015

It is a challenging problem to search the intended images from a large number of candidates. Content based image retrieval (CBIR) is the most promising way to tackle this problem, where the most important topic is to measure the similarity of images so as to cover the variance of shape, color, pose, illumination etc. While previous works made significant progresses, their adaption ability to dataset is not fully explored. In this paper, we propose a similarity learning method on the basis of probabilistic generative model, i.e., probabilistic latent semantic analysis (PLSA). It first derives Fisher kernel, a function over the parameters and variables, based on PLSA. Then, the parameters are determined through simultaneously maximizing the log likelihood function of PLSA and the retrieval performance over the training dataset. The main advantages of this work are twofold: (1) deriving similarity measure based on PLSA which fully exploits the data distribution and Bayes inference; (2) learning model parameters by maximizing the fitting of model to data and the retrieval performance simultaneously. The proposed method (PLSA-FK) is empirically evaluated over three datasets, and the results exhibit promising performance.

• The Journal of the Korea Contents Association
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• v.13 no.5
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• pp.436-442
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• 2013

Recently, online environment is actively used so it will be needed to identify the factors influencing satisfaction for continuous success. In this paper, we explain the users' satisfaction of online purchasing IT and verify them through the empirical analysis. We select basic variables from TAM (Technology Acceptance Model) and add specific variables from former research about user's trait. We chose satisfaction, perceived easiness, perceived usefulness, regret and maximizing tendency. Data collected from 150 user who had prior experiences with online purchasing IT were empirically tested against the research model using partial least square (PLS). The results show that perceived easiness, perceived usefulness and regret are significantly related to satisfaction and maximizing tendency has positive impact on perceived easiness, perceived usefulness and regret significantly.

• Steel and Composite Structures
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• v.28 no.4
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• pp.509-516
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• 2018

The differential quadrature (DQ) and teaching-learning based optimization (TLBO) methods are coupled to introduce a hybrid numerical method for maximizing fundamental natural frequency of laminated composite skew plates. The fiber(s) orientations are selected as design variable(s). The first-order shear deformation theory (FSDT) is used to obtain the governing equations of the plate. The equations of motion and the related boundary conditions are discretized in space domain by employing the DQ method. The discretized equations are transferred from the time domain into the frequency domain to obtain the fundamental natural frequency. Then, the DQ solution is coupled with the TLBO method to find the maximum frequency of the plate and its related optimum stacking sequences of the laminate. Convergence and applicability of the proposed method are shown and the optimum fundamental frequency parameter of the plates with different skew angle, boundary conditions, number of layers and aspect ratio are obtained. The obtained results can be used as a benchmark for further studies.

• Journal of information and communication convergence engineering
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• v.15 no.2
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• pp.79-84
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• 2017

In a cognitive radio network (CRN), cognitive radio users (CUs) should be powered by a small battery for their operations. The operations of the CU often include spectrum sensing and data transmission. The spectrum sensing process may help the CU avoid a collision with the primary user (PU) and may save the energy that is wasted in transmitting data when the PU is present. However, in a time-slotted manner, the sensing process consumes energy and reduces the time for transmitting data, which degrades the achieved throughput of the CRN. Subsequently, the sensing process does not always offer an advantage in regards to throughput to the CRN. In this paper, we propose a scheme to find an optimal policy (i.e., perform spectrum sensing before transmitting data or transmit data without the sensing process) for maximizing the achieved throughput of the CRN. In the proposed scheme, the data collection period is considered as the main factor effecting on the optimal policy. Simulation results show the advantages of the optimal policy.

• Journal of the Economic Geographical Society of Korea
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• v.13 no.3
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• pp.487-497
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• 2010

This paper estimates theoretically optimal sizes of urban population for major metropolitan areas using an urban economy system with utility maximizing household, profit maximizing producer and government providing public goods. This finds that the optimal size of urban population is determined by technological levels and public services. The population sizes of Seoul, Busan, Daegu and Incheon are higher than their optimal levels, while Gwangju, Daejeon and Ulsan need to increase the population for production efficiency.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.5
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• pp.197-203
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• 2008

In this work, we present a novel genetic approach to solve the problem of combining power control and data rate transmission adjustment for the performance enhancement of the next generation CDMA system. We obtained the optimal solution of multi rate and power control problem by compromising slightly on SIR limit values. The proposed algorithm was able to handle many more users with comparable or faster convergent service. While this paper considered two kinds of fitness function such as maximizing the total transmission data rate and maximizing the acceptable mobiles of CDMA cellular network, the evaluation function combining these two cases or others can be also easily implemented. The simulation results showed the effectiveness and validity of our approach.

• Structural Engineering and Mechanics
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• v.67 no.3
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• pp.301-310
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• 2018

In this paper, a multi-objective multiparameter optimization procedure is developed by combining rigorously developed metamodels with an evolutionary search algorithm-Genetic Algorithm (GA). Response surface methodology (RSM) is used for developing the metamodels to replace the tedious finite element analyses. A nine-node isoparametric plate bending element is used for conducting the finite element simulations. Highly accurate numerical data from an author compiled FORTRAN finite element program is first used by the RSM to develop second-order mathematical relations. Four material parameters-${\frac{E_1}{E_2}}$, ${\frac{G_{12}}{E_2}}$, ${\frac{G_{23}}{E_2}}$ and ${\upsilon}_{12}$ are considered as the independent variables while simultaneously maximizing fundamental frequency, ${\lambda}_1$ and frequency separation between the $1^{st}$ two natural modes, ${\lambda}_{21}$. The optimal material combination for maximizing ${\lambda}_1$ and ${\lambda}_{21}$ is predicted by using a multi-objective GA. A general sensitivity analysis is conducted to understand the effect of each parameter on the desired response parameters.

• Journal of KIISE:Computer Systems and Theory
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• v.32 no.11_12
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• pp.646-653
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• 2005

This paper proposes that wavelength assignment algorithm maximizes residual links considering more real wavelength conversion in the WDM network. The existing algorithms are inefficient that they ignore wavelength conversion ability in each nodes and fixed uniformly wavelength conversion ability on all nodes. Proposed method makes a ring shape that maximize available residual links considering wavelength conversion ability in each nodes. Maximizing residual links can assign a wavelength for any path require. Then blocking probability is reduced by the maximum 19 percent and wavelength conversion number is improved by about 40 percent. These can be showed in performance evaluation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.5013-5034
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• 2019

In the Peer-to-Peer (P2P) streaming systems, peers randomly form a network overlay to share video resources with a data scheduling scheme. A data scheduling scheme can have a great impact on system performance, which should achieve two optimal objectives at the same time ideally. The two optimization objectives are to improve the perceived video quality and maximize the network throughput, respectively. Maximizing network throughput means improving the utilization of peer's upload bandwidth. However, maximizing network throughput will result in a reduction in the perceived video quality, and vice versa. Therefore, to achieve the above two objects simultaneously, we proposed a new data scheduling scheme based on multi-objective particle swarm optimization data scheduling scheme, called MOPSO-DS scheme. To design the MOPSO-DS scheme, we first formulated the data scheduling optimization problem as a multi-objective optimization problem. Then, a multi-objective particle swarm optimization algorithm is proposed by encoding the neighbors of peers as the position vector of the particles. Through extensive simulations, we demonstrated the MOPSO-DS scheme could improve the system performance effectively.