• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권11호
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• pp.5631-5652
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• 2019

The existing defense strategy selection methods based on game theory basically select the optimal defense strategy in the form of mixed strategy. However, it is hard for network managers to understand and implement the defense strategy in this way. To address this problem, we constructed the incomplete information stochastic game model for the dynamic analysis to predict multi-stage attack-defense process by combining Bayesian game theory and the Markov decision-making method. In addition, the payoffs are quantified from the impact value of attack-defense actions. Based on previous statements, we designed an optimal defense strategy selection method. The optimal defense strategy is selected, which regards defense effectiveness as the criterion. The proposed method is feasibly verified via a representative experiment. Compared to the classical strategy selection methods based on the game theory, the proposed method can select the optimal strategy of the multi-stage attack-defense process in the form of pure strategy, which has been proved more operable than the compared ones.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권4호
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• pp.1426-1447
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• 2021

The payoff game between task publishers and users in the mobile crowdsensing environment is a hot topic of research. A optimal payoff selection model based on stochastic evolutionary game is proposed. Firstly, the process of payoff optimization selection is modeled as a task publisher-user stochastic evolutionary game model. Secondly, the low-quality data is identified by the data quality evaluation algorithm, which improves the fitness of perceptual task matching target users, so that task publishers and users can obtain the optimal payoff at the current moment. Finally, by solving the stability strategy and analyzing the stability of the model, the optimal payoff strategy is obtained under different intensity of random interference and different initial state. The simulation results show that, in the aspect of data quality evaluation, compared with BP detection method and SVM detection method, the accuracy of anomaly data detection of the proposed model is improved by 8.1% and 0.5% respectively, and the accuracy of data classification is improved by 59.2% and 32.2% respectively. In the aspect of the optimal payoff strategy selection, it is verified that the proposed model can reasonably select the payoff strategy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제3권2호
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• pp.147-162
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• 2009

In this paper, we propose a Cooperative Transmission Strategy using Entropy-based Relay Selection in Mobile Ad-hoc Wireless Sensor Networks(MAWSN) with Rayleigh Fading Environments. The main features and contributions of the proposed cooperative transmission strategy are as follows. First, entropy-based relay selection is used to improve data transmission reliability from a source node to a destination node. Second, we present a theoretical analysis model for the proposed cooperative transmission strategy with the outage probability of the end-to-end performance. The performance of our protocol is evaluated using analysis and simulation.

• Asian-Australasian Journal of Animal Sciences
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• 제29권5호
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• pp.640-645
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• 2016

The objective of this study was to evaluate the present conventional selection program of a swine nucleus farm and compare it with a new selection strategy employing genomic enhanced breeding value (GEBV) as the selection criteria. The ZPLAN+ software was employed to calculate and compare the genetic gain, total cost, return and profit of each selection strategy. The first strategy reflected the current conventional breeding program, which was a progeny test system (CS). The second strategy was a selection scheme based strictly on genomic information (GS1). The third scenario was the same as GS1, but the selection by GEBV was further supplemented by the performance test (GS2). The last scenario was a mixture of genomic information and progeny tests (GS3). The results showed that the accuracy of the selection index of young boars of GS1 was 26% higher than that of CS. On the other hand, both GS2 and GS3 gave 31% higher accuracy than CS for young boars. The annual monetary genetic gain of GS1, GS2 and GS3 was 10%, 12%, and 11% higher, respectively, than that of CS. As expected, the discounted costs of genomic selection strategies were higher than those of CS. The costs of GS1, GS2 and GS3 were 35%, 73%, and 89% higher than those of CS, respectively, assuming a genotyping cost of $120. As a result, the discounted profit per animal of GS1 and GS2 was 8% and 2% higher, respectively, than that of CS while GS3 was 6% lower. Comparison among genomic breeding scenarios revealed that GS1 was more profitable than GS2 and GS3. The genomic selection schemes, especially GS1 and GS2, were clearly superior to the conventional scheme in terms of monetary genetic gain and profit.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권12호
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• pp.3133-3151
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• 2012

The optimal price strategy selection of two bounded rational cognitive mobile virtual network operators (MVNOs) in a duopoly spectrum sharing market is investigated. The bounded rational operators dynamically compete to sell the leased spectrum to secondary users in order to maximize their profits. Meanwhile, the secondary users' heterogeneous preferences to rate and price are taken into consideration. The evolutionary game theory (EGT) is employed to model the dynamic price strategy selection of the MVNOs taking into account the response of the secondary users. The behavior dynamics and the evolutionary stable strategy (ESS) of the operators are derived via replicated dynamics. Furthermore, a reward and punishment mechanism is developed to optimize the performance of the operators. Numerical results show that the proposed evolutionary algorithm is convergent to the ESS, and the incentive mechanism increases the profits of the operators. It may provide some insight about the optimal price strategy selection for MVNOs in the next generation cognitive wireless networks.

• 지구물리와물리탐사
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• 제14권3호
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• pp.214-219
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• 2011

본 논문에서는 시간영역에서 분리된 잔여 파동장을 이용하여 주파수영역 파형역산을 수행하였다. 시간영역 잔여 파동장들을 절대값의 크기에 따라 정렬하여 분류하고, 이를 여러 개의 그룹으로 분리하였다. 분리된 잔여 파동장들은 각 그룹별로 목적함수의 경사 방향을 정규화한 후 평균하기 때문에 통상적인 잔여 파동장에서 작은 크기를 가지는 파동장들을 상대적으로 강조하는 효과가 있고, 이는 파형역산 시 심부구조의 이미지 향상에 도움을 준다. 파형역산은 시간영역에서 분리된 잔여 파동장을 이용하여 주파수영역에서 수행되며, 목적함수의 경사방향은 구조보정에서 많이 쓰이는 역전파 기법을 적용하여 계산된다. 본 연구에서 제안한 알고리듬의 타당성을 확인하기 위하여 SEG/EAGE 암염 모델과 Marmousi 모델을 이용하여 파형역산을 수행하였다. 역산 결과를 통해 제안된 알고리즘이 일반적인 주파수영역 파형역산에 비해 심부구조에 대하여 향상된 결과를 제시함을 확인하였다.

• Journal of information and communication convergence engineering
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• 제9권5호
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• pp.491-499
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• 2011

In this paper, we offer a new strategic portfolio model for national IT R&D project selection in Korea. A risk and return (R-R) portfolio model was developed using an objectively quantified index on the two axes of risk and return, in order to select a strategic project and allocate resources in compliance with a national IT R&D strategy. We strategize using the R-R portfolio model to solve the non-strategy and subjectivity problems of the existing national R&D project selection model. We also use the quantified evaluation index of the IT technology road map (TRM) and the technical level reports (TLR) for the subjectivity of project selection, and try to discover the weights using the analytic hierarchy process (AHP). In addition, we intend to maximize the chance for a successful national IT R&D project, by selecting a strategic portfolio project and balancing the allocation of resources effectively and objectively.

• 전자공학회논문지B
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• 제31B권8호
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• pp.109-118
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• 1994

We proposed new field/frame mode selection quantization and buffer control strategy for interlaced digital HDTV image coding system. The field/frame mode selection is performed based on the mean square error and number of generated bits for each superblock. The quantization factor for each superblock is determined by the characteristics of human visual system and buffer status. The statistical characteristics of the number of generated bits for basis block and the prediction of buffer status are used for buffer control. Simulation results show that the proposed field/frame mode selection and rate buffer control. Simulation results show that the proposed field/frame mode selection and rate buffer control strategy have good subjective image quality and have stable buffer status.

• 한국정보기술학회 영문논문지
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• 제10권1호
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• pp.1-14
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• 2020

Antenna selection is a method to enhance the performance of spatial multiplexing multiple-input multiple-output (MIMO) systems, which can achieve the diversity order of the full MIMO systems. Although various selection criteria have been studied in the literature, they should be adjusted to the detection operation implemented at the receiver. In this paper, antenna selection methods that optimize the post-processing signal-to-noise ratio (SNR) and eigenvalue are considered for the lattice reduction (LR)-based receiver. To develop a complexity-efficient antenna selection algorithm, the incremental selection strategy is adopted. Moreover, for improvement of performance, an additional iterative selection method is presented in combination with an incremental strategy.

• 제어로봇시스템학회논문지
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• 제6권12호
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• pp.1079-1085
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• 2000

In this paper, we propose a method of cooperative control (T-cell modeling) and selection of group behavior strategy (B-cell modeling) based on immune system in distributed autonomous robotic system (DARS). An immune system is the living bodys self-protection and self-maintenance system. these features can be applied to decision making of the optimal swarm behavior in a dynamically changing environment. For applying immune system to DARS, a robot is regarded as a B-cell, each environmental condition as an antigen, a behavior strategy as an antibody, and control parameter as a T-cell, respectively. When the environmental condition (antigen) changes, a robot selects an appropriate behavior strategy (antibody). And its behavior strategy is stimulated and suppressed by other robots using communication (immune network). Finally, much stimulated strategy is adopted as a swarm behavior strategy. This control scheme is based on clonal selection and immune network hypothesis, and it is used for decision making of the optimal swarm strategy. Adaptation ability of the robot is enhanced by adding T-cell model as a control parameter in dynamic environments.