• 산업경영시스템학회지
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• 제42권3호
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• pp.217-231
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• 2019

The purpose of this study is to analyze the factors influencing consumers' selection of web-toon service through AHP (Analytic Hierarchy Process) analysis and to provide the strategy of web-toon service. To accomplish this study, theories, existing research and references related to AHP were sufficiently examined and selected the factors in the selection criteria. Surveys from consumers who used the web-toon service were conducted with selected factors. Through this, the results were analyzed by AHP analysis to find out the weighting values and the differences were examined and analyzed. The highest weighting factor in the first layer that consists of web-toon service was cinematic quality. The cinematic quality was the most important factor in the selection criteria of customers who use the web-toon service regardless of their preferred genre. Furthermore, it was confirmed that the weighting value or ranking changed in the second layer by genre. In this study, the effective basis of strategy were suggested by ranking the quantitative selection factors according to the preferred genre of consumers using web-toon services. In addition, This research provides some practical implications. That is, the web-toon service provider can easily recognize and respond to the customer's requirements, which factors are important when the customer selects a specific genre from the web-toon genre.

• Journal of Communications and Networks
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• 제18권3호
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• pp.411-419
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• 2016

This article investigates the problem of distributed channel selection in opportunistic spectrum access networks from a perspective of interference minimization. The traditional physical (PHY)-layer interference model is for information theoretic analysis. When practical multiple access mechanisms are considered, the recently developed binary medium access control (MAC)-layer interference model in the previous work is more useful, in which the experienced interference of a user is defined as the number of competing users. However, the binary model is not accurate in mathematics analysis with poor achievable performance. Therefore, we propose a real-valued one called stochastic MAC-layer interference model, where the utility of a player is defined as a function of the aggregate weight of the stochastic interference of competing neighbors. Then, the distributed channel selection problem in the stochastic MAC-layer interference model is formulated as a weighted stochastic MAC-layer interference minimization game and we proved that the game is an exact potential game which exists one pure strategy Nash equilibrium point at least. By using the proposed stochastic learning-automata based uncoupled algorithm with heterogeneous learning parameter (SLA-H), we can achieve suboptimal convergence averagely and this result can be verified in the simulation. Moreover, the simulated results also prove that the proposed stochastic model can achieve higher throughput performance and faster convergence behavior than the binary one.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 G
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• pp.3024-3026
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• 1999

This paper presents a cooperative action controller of a multi-agent system. To achieve an object, i.e. win a game, it is necessary that a robot has its own roles, actions and work with each other. The presented incorporated action controller consists of the role selection, action selection and execution layer. In the first layer, a fuzzy logic controller is used. Each robot selects its own action and makes its own path trajectory in the second layer. In the third layer, each robot performs their own action based on the velocity information which is sent from main computer. Finally, simulation shows that each robot selects proper roles and incorporates actions by the proposed controller.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2016년도 춘계학술발표대회
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• pp.633-635
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• 2016

Convolutional Networks (ConvNets) are powerful models that learn hierarchies of visual features, which could also be used to obtain image representations for transfer learning. The basic pipeline for transfer learning is to first train a ConvNet on a large dataset (source task) and then use feed-forward units activation of the trained ConvNet as image representation for smaller datasets (target task). Our key contribution is to demonstrate superior performance of multiple ConvNet layer features over single ConvNet layer features. Combining multiple ConvNet layer features will result in more complex feature space with some features being repetitive. This requires some form of feature selection. We use AdaBoost with single stumps to implicitly select only distinct features that are useful towards classification from concatenated ConvNet features. Experimental results show that using multiple ConvNet layer activation features instead of single ConvNet layer features consistently will produce superior performance. Improvements becomes significant as we increase the distance between source task and the target task.

• Journal of Information Technology Applications and Management
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• 제17권1호
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• pp.141-155
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• 2010

We develop an intelligent information system in a multilayer electronic supply chain network. Using the internet for supply chain management (SCM) is a key interest for contemporary managers and researchers. It has been realized that the internet can facilitate SCM by making real time information available and enabling collaboration between trading partners. Here, we propose a multi-agent system to analyze the performance of the elements of a supply network based on the attributes of the information flow. Each layer consists of elements which are differentiated by their performance throughout the supply network. The proposed agents measure and record the performance flow of elements considering their web interactions for a dynamic route selection. A dynamic programming approach is applied to determine the optimal route for a customer in the end-user layer.

• Journal of Information Processing Systems
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• 제16권6호
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• pp.1261-1270
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• 2020

The physical security layer of industrial wireless sensor networks in the event of an eavesdropping attack has been investigated in this paper. An optimal sensor selection scheme based on the maximum channel capacity is proposed for transmission environments that experience Nakagami fading. Comparing the intercept probabilities of the traditional round robin (TRR) and optimal sensor selection schemes, the system secure performance is analyzed. Simulation results show that the change in the number of sensors and the eavesdropping ratio affect the convergence rate of the intercept probability. Additionally, the proposed optimal selection scheme has a faster convergence rate compared to the TRR scheduling scheme for the same eavesdropping ratio and number of sensors. This observation is also valid when the Nakagami channel is simplified to a Rayleigh channel.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 B
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• pp.924-926
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• 1999

To reduce chattering in sliding mode control, a boundary layer around the sliding surface is used, and a continuous control is applied within the boundary. In this paper, a method of determining the sliding mode controller switching gains and the width of boundary layer is presented. Contrary to the trial and error selection of the switching gains and the width of boundary layer, the selection in the presented work is done using genetic algorithms. Simulation results show that the system performance has been improved.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권11호
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• pp.4623-4643
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• 2015

This paper considers physical-layer security protocols in multicast cognitive radio (CR) networks. In particular, we propose dual-hop cooperative decode-and-forward (DF) and randomize-and-forward (RF) schemes using partial relay selection method to enhance secrecy performance for secondary networks. In the DF protocol, the secondary relay would use same codebook with the secondary source to forward the source's signals to the secondary destination. Hence, the secondary eavesdropper can employ either maximal-ratio combining (MRC) or selection combining (SC) to combine signals received from the source and the selected relay. In RF protocol, different codebooks are used by the source and the relay to forward the source message secretly. For each scheme, we derive exact and asymptotic closed-form expressions of secrecy outage probability (SOP), non-zero secrecy capacity probability (NzSCP) in both independent and identically distributed (i.i.d.) and independent but non-identically distributed (i.n.i.d.) networks. Moreover, we also give a unified formula in an integral form for average secrecy capacity (ASC). Finally, our derivations are then validated by Monte-Carlo simulations.

• ETRI Journal
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• 제34권3호
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• pp.388-398
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• 2012

Three-dimensional (3D) memories using through-silicon vias (TSVs) will likely be the first commercial applications of 3D integrated circuit technology. A 3D memory yield can be enhanced by vertical redundancy sharing strategies. The methods used to select memory dies to form 3D memories have a great effect on the 3D memory yield. Since previous die-selection methods share redundancies only between neighboring memory dies, the opportunity to achieve significant yield enhancement is limited. In this paper, a novel die-selection method is proposed for multilayer 3D memories that shares redundancies among all of the memory dies by using additional TSVs. The proposed method uses three selection conditions to form a good multi-layer 3D memory. Furthermore, the proposed method considers memory fault characteristics, newly detected faults after bonding, and multiple memory blocks in each memory die. Simulation results show that the proposed method can significantly improve the multilayer 3D memory yield in a variety of situations. The TSV overhead for the proposed method is almost the same as that for the previous methods.

• International Journal of Quality Innovation
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• 제3권2호
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• pp.113-131
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• 2002

The relationships among multi-dimensional data (such as medical examination data) with ambiguity and variation are difficult to explore. The traditional approach to building a data classification system requires the formulation of rules by which the input data can be analyzed. The formulation of such rules is very difficult with large sets of input data. This paper first describes two classification approaches using back-propagation (BP) neural network and Mahalanobis distance (MD) classifier, and then proposes two classification approaches for multi-dimensional feature selection. The first one proposed is a feature selection procedure from the trained back-propagation (BP) neural network. The basic idea of this procedure is to compare the multiplication weights between input and hidden layer and hidden and output layer. In order to simplify the structure, only the multiplication weights of large absolute values are used. The second approach is Mahalanobis-Taguchi system (MTS) originally suggested by Dr. Taguchi. The MTS performs Taguchi's fractional factorial design based on the Mahalanobis distance as a performance metric. We combine the automatic thresholding with MD: it can deal with a reduced model, which is the focus of this paper In this work, two case studies will be used as examples to compare and discuss the complete and reduced models employing BP neural network and MD classifier. The implementation results show that proposed approaches are effective and powerful for the classification.