• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.6
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• pp.2680-2696
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• 2018

The collaborative content caching system is an effective solution developed in recent years to reduce transmission delay and network traffic. In order to decrease the service end-to-end transmission delay for future 5G ultra-dense networks (UDN), this paper proposes a novel service migration method that can guarantee the continuity of service and simultaneously reduce the traffic flow in the network. In this paper, we propose a service migration optimization model that minimizes the cumulative transmission delay within the constraints of quality of service (QoS) guarantee and network condition. Subsequently, we propose an improved firefly algorithm to solve this optimization problem. Simulation results show that compared to traditional collaborative content caching schemes, the proposed algorithm can significantly decrease transmission delay and network traffic flow.

• Information Systems Review
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• v.8 no.1
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• pp.1-24
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• 2006

Now, the Korean footwear industry needs the concrete enhancement of its competitive edge for regaining of its previous well known reputation. For this purpose, this study emphasizes the cooperation between the members through the supply chain of Korean footwear industry, as a c-SCM(Collaborative Supply Chain Management) by aid of information system. The key issue will be how well to coordinate operations flow not only through internal process stages, but also through entire external supply chain stages. In other words, the target goal must be the system optimization through the entire supply chain beyond the local optimization of internal supply chain process. We, at first, analyze the traditional structure of supply chain in Korean footwear industry and find out critical problems, and then, we develop the collaborative information framework in conjunction with several collaborative process modules. The suggested collaborative production & supply planning system was designed for sharing information and it is based on ebXML(electronic business eXtensible Markup Language) framework. In this way, the enhancement of the efficiency and competitiveness can be expected through the synergy effect of coordination of information and material flow, the reduction of lead times, and production costs.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.384-386
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• 2009

This paper introduces an effectively optimized Fuzzy model identification by means of complex and nonlinear system applying PSO algorithm. In other words, we use PSO(Particle Swarm Optimization) for identification of Fuzzy model structure and parameter. PSO is an algorithm that follows a collaborative population-based search model. Each particle of swarm flies around in a multidimensional search space looking for the optimal solution. Then, Particles adjust their position according to their own and their neighboring-particles experience. This paper identifies the premise part parameters and the consequence structures that have many effects on Fuzzy system based on PSO. In the premise parts of the rules, we use triangular. Finally we evaluate the Fuzzy model that is widely used in the standard model of gas data and sew data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.11
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• pp.4453-4468
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• 2015

This paper investigates optimization-based base station (BS) placement. An optimization model is defined and the BS placement problem is transformed to a lexicographical stratified programming (LSP) model for a given trajectory, according to different accuracy requirements. The feasible region for BS deployment is obtained from the positioning system requirement, which is also solved with signal coverage problem in BS placement. The LSP mathematical model is formulated with the average geometric dilution of precision (GDOP) as the criterion. To achieve an optimization solution, a tolerant factor based complete stratified series approach and grid searching method are utilized to obtain the possible optimal BS placement. Because of the LSP model utilization, the proposed algorithm has wider application scenarios with different accuracy requirements over different trajectory segments. Simulation results demonstrate that the proposed algorithm has better BS placement result than existing approaches for a given trajectory.

• Journal of Information Technology Application
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• v.1
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• pp.173-211
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• 1999

The task of classification permeates all walks of life, from business and economics to science and public policy. In this context, nonlinear techniques from artificial intelligence have often proven to be more effective than the methods of classical statistics. The objective of knowledge discovery and data mining is to support decision making through the effective use of information. The automated approach to knowledge discovery is especially useful when dealing with large data sets or complex relationships. For many applications, automated software may find subtle patterns which escape the notice of manual analysis, or whose complexity exceeds the cognitive capabilities of humans. This paper explores the utility of a collaborative learning approach involving integrated models in the preprocessing and postprocessing stages. For instance, a genetic algorithm effects feature-weight optimization in a preprocessing module. Moreover, an inductive tree, artificial neural network (ANN), and k-nearest neighbor (kNN) techniques serve as postprocessing modules. More specifically, the postprocessors act as second0order classifiers which determine the best first-order classifier on a case-by-case basis. In addition to the second-order models, a voting scheme is investigated as a simple, but efficient, postprocessing model. The first-order models consist of statistical and machine learning models such as logistic regression (logit), multivariate discriminant analysis (MDA), ANN, and kNN. The genetic algorithm, inductive decision tree, and voting scheme act as kernel modules for collaborative learning. These ideas are explored against the background of a practical application relating to financial fraud management which exemplifies a binary classification problem.

• The KIPS Transactions:PartC
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• v.13C no.6 s.109
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• pp.749-756
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• 2006

Broadcast nature of wireless medium and path-loss reduction create a favourable condition for collaborative communications (CC) among single-antenna users to gain the powerful benefits of multi-antenna system without the demand for physical arrays. This paper proposes a CC strategy adapting to the propagation environment changes by optimizing the transmit signal amplification factors to simplify the structure of maximum likelihood (ML) detector and to obtain the minimum error probability as well. The closed-form BER expression was also derived and compared to the simulation results to evaluate the performance of the suggested solution. A variety of numerical results revealed the cooperation significantly outperforms non-cooperative counterpart under flat Rayleigh fading channel plus AWGN (Additive White Gaussian Noise).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2381-2399
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• 2019

Traditional recommendation algorithms on Collaborative Filtering (CF) mainly focus on the rating prediction with explicit ratings, and cannot be applied to the top-N recommendation with implicit feedbacks. To tackle this problem, we propose a new collaborative filtering approach namely Maximize MAP with Matrix Factorization (MFMAP). In addition, in order to solve the problem of non-smoothing loss function in learning to rank (LTR) algorithm based on pairwise, we also propose a smooth MAP measure which can be easily implemented by standard optimization approaches. We perform experiments on three different datasets, and the experimental results show that the performance of MFMAP is significantly better than other recommendation approaches.

• International journal of advanced smart convergence
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• v.12 no.2
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• pp.56-66
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• 2023

The goal of deep learning is to extract complex features from multidimensional data use the features to create models that connect input and output. Deep learning is a process of learning nonlinear features and functions from complex data, and the user data that is employed to train deep learning models has become the focus of privacy concerns. Companies that collect user's sensitive personal information, such as users' images and voices, own this data for indefinite period of times. Users cannot delete their personal information, and they cannot limit the purposes for which the data is used. The study has designed a deep learning method that employs privacy protection technology that uses distributed collaborative learning so that multiple participants can use neural network models collaboratively without sharing the input datasets. To prevent direct leaks of personal information, participants are not shown the training datasets during the model training process, unlike traditional deep learning so that the personal information in the data can be protected. The study used a method that can selectively share subsets via an optimization algorithm that is based on modified distributed stochastic gradient descent, and the result showed that it was possible to learn with improved learning accuracy while protecting personal information.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.5
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• pp.482-490
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• 2009

In this paper, the author proposes a new method for acoustic radiation optimum design to minimize noise from a vibrating panel-like structure using a collaborative population-based search method called the particle swarm optimization algorithm(PSOA). The PSOA is a parallel evolutionary computation technique initially developed by Kennedy and Eberhart. The acoustic radiation optimization method based on the PSOA consists of two processes. In the first process, the acoustic radiation analysis by an integrated p-version FEM/BEM, which was developed by using MATLAB, is performed to evaluate the exterior acoustic radiation field of the panel. The second process is to search the optimum design variables: 1) Shape of Bezier curves and 2) Shape and position of ribs, to minimize noise from the panel using the PSOA. The optimization method based on the PSOA is compared to that based on the steady state genetic algorithm(SSGA) in order to verify the effectiveness and validity of the optimal solution by PSOA. Finally, it is shown that the optimal designs of the panel obtained by using the PSOA can achieve effective reductions in radiated sound power.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.35 no.12
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• pp.1121-1128
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• 2007

MDO(Multidisciplinary Design and Optimization) can be applied for design of complex systems such as aircraft and SLV(Space Launch Vehicle). MDO System can be an integrated environment or a system, which is for synthetic and instantaneous analysis and design optimization in various design fields. MDO System has to efficiently use and integrate distributed resources such as various analysis codes, optimization codes, CAD, DBMS, GUI, and etc. in heterogeneous environments. In this paper, we present Web Services-based MDO System that integrates resources for MDO using Globus Toolkit and provides organic autonomous execution using automation technique such as Workflow system and agent. And also, it provides collaborative design environment through web user interfaces.