• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.8
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• pp.1719-1726
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• 2002

Design variables and design parameters are rarely deterministic in practice. Robust optimal design takes into consideration of the uncertainties in the design variables and parameters. Robust optimization methodology with probability constraints requires a lot of system analyses fer calculating failure probability of each constraint. By introducing an envelope function to reduce the number of constraints, efficiency of robust optimization techniques can be considerably improved. Through four illustrative examples, it is shown that the number of system analyses is greatly decreased while little differences in the optimum results are observed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.10
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• pp.2457-2464
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• 2015

Distributed Networked Control Systems (NCSs) through wireless networks have a tremendous potential to improve the efficiency of various control systems. In this paper, we define the State Update Interval (SUI) as the elapsed time between successful state vector reports derived from the NCSs. A simple expression of the SUI is derived to characterize the key interactions between the control and communication layers. This performance measure is used to formulate a novel optimization problem where the objective function is the probability to meet the SUI constraint and the decision parameter is the channel access probability. We prove the existence and uniqueness of the optimal channel access probability of the optimization problem. Furthermore, the optimal channel access probability for NCSs is lower than the channel access probability to maximize the throughput. Numerical results indicate that the improvement of the success probability to meet the SUI constraint using the optimal channel access probability increases as the number of nodes increases with respect to that using the channel access probability to maximize the throughput.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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• pp.1263-1274
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• 2021

In online competitive social networks, each user can be influenced by different competing influencers and consequently chooses different products. But their interest may change over time and may have swings between different products. The existing influence spreading models seldom take into account the time-related shifts. This paper proposes a minimum cost influence maximization algorithm based on the competitive transition probability. In the model, we set a one-dimensional vector for each node to record the probability that the node chooses each different competing influencer. In the process of propagation, the influence maximization on Competitive Linear Threshold (IMCLT) spreading model is proposed. This model does not determine by which competing influencer the node is activated, but sets different weights for all competing influencers. In the process of spreading, we select the seed nodes according to the cost function of each node, and evaluate the final influence based on the competitive transition probability. Experiments on different datasets show that the proposed minimum cost competitive influence maximization algorithm based on IMCLT spreading model has excellent performance compared with other methods, and the computational performance of the method is also reasonable.

• ETRI Journal
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• v.27 no.6
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• pp.733-746
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• 2005

In the context of multi-protocol label switching (MPLS) traffic engineering, this paper proposes a scalable constraintbased shortest path first (CSPF) routing algorithm with multiple QoS metrics. This algorithm, called the multiple constraint-based shortest path first (M_CSPF) algorithm, provides an optimal route for setting up a label switched path (LSP) that meets bandwidth and end-to-end delay constraints. In order to maximize the LSP accommodation probability, we propose a link weight computation algorithm to assign the link weight while taking into account the future traffic load and link interference and adopting the concept of a critical link from the minimum interference routing algorithm. In addition, we propose a bounded order assignment algorithm (BOAA) that assigns the appropriate order to the node and link, taking into account the delay constraint and hop count. In particular, BOAA is designed to achieve fast LSP route computation by pruning any portion of the network topology that exceeds the end-to-end delay constraint in the process of traversing the network topology. To clarify the M_CSPF and the existing CSPF routing algorithms, this paper evaluates them from the perspectives of network resource utilization efficiency, end-to-end quality, LSP rejection probability, and LSP route computation performance under various network topologies and conditions.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.61.1-61
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• 2001

This paper describes an efficient ambiguity searching method using additional constraints in 3-dimentional attitude finding GPS receiver design. For determining the integer ambiguity, the baseline length constraint, the angle constraint, the velocity constraint and the attitude constraint can be used for reducing the searching space. This paper describes the searching space algebraically and graphically. It is confirmed that the described restrictions are reasonable and the speed and the probability of ambiguity fixing are improved when the restricted searching spaces are applied Moreover, it is possible to design receivers of better quality by applying the method proposed in this paper.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.1 s.173
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• pp.215-224
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• 2000

In order to reduce the variation effects of uncertainties in the engineering environments, new robust optimization method, which considers the uncertainties in design process, is proposed. Both design variables and system parameters are considered as random variables about their nominal values. To ensure the robustness of performance function, a new objective is set to minimize the variance of that function. Constraint variations are handled by introducing probability constraints. Probability constraints are solved by the advanced first order second moment (AFOSM) method based on the reliability theory. The proposed robust optimization method has an advantage that the second derivatives of the constraints are not required. The suggested method is examined by solving three examples and the results are compared with those for deterministic case and those available in literature.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.14 no.24
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• pp.1-5
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• 1991

Various linear cost models have been proposed that can be used to determine a sampling plan by attributes. This paper is concerned with this sampling cost model when the probability that the number of nonconforming item is smaller than the break-even quality level is known. In addition to this situation, a constraint by AOQL is considered. Under these conditions, optimal sampling plan which minimize the average cost per lot is suggested.

• Structural Engineering and Mechanics
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• v.13 no.5
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• pp.479-490
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• 2002

The structural dynamic optimization problem based on probability is studied. Considering the randomness of structural physical parameters and the given constraint values, we develop a dynamic optimization mathematical model of engineering structures with the probability constraints of frequency, forbidden frequency domain and the vibration mode. The sensitivity of structural dynamic characteristics based on probability is derived. Two examples illustrate that the optimization model and the method applied are rational and efficient.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.1A
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• pp.13-23
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• 2007

In this paper, a packet scheduling scheme that supports real-time traffic having multi-level delay constraints in OFDMA systems is proposed. The proposed scheme pursues to satisfy the delay constraint first, and then manage the residual radio resource in order to enhance the overall throughput. A parameters named tolerable delay time (TDT) is newly defined to deal with the differentiated behaviors of packet scheduling according to the delay constraint level. Assuming that the packets violating the delay constraint are discarded, the proposed scheme is evaluated in terms of the packet loss probability, throughput, channel utilization. It is then compared with existing schemes for real-time traffic support such as the Exponential Scheduling (EXP) scheme, the Modified Largest Weighted Delay First (M-LWDF) scheme, and the Round robin scheme. The numerical results show that the proposed scheduling scheme performs much better than the aforementioned scheduling schemes in terms of the packet loss probability, while slightly better in terms of throughput and channel utilization.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.65-68
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• 2000

It is well-known that the decision rule in the mini-mum mean-squares-error decision feedback equalizer(MMSE-DFE) is biased, and therefore suboptimum with respect to error probability. We present a new family of algorithms that solve the bias problem in the adaptive DFE. A novel constraint, called the constant-norm con-straint, is introduced unifying the quadratic constraint and the monic one. A new cost function based on the constant-norm constraint and Lagrange multiplier is defined. Minimizing the cost function gives birth to a new family of unbiased adaptive DFE. The simula-tion results demonstrate that the proposed method in-deed produce unbiased solution in the presence of noise while keeping very simple both in computation and im-plementation.