• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.4A
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• pp.271-281
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• 2005

This paper for establishing the reliability of the TPC command is introduced, where the soft symbol of the TPC command itself is directed used as a reliability indicator. In addition to the new reliability estimation, the concept of parallel use of TPC algorithms is presented. The results show that the soft symbol reliability estimation decrease the $P_{tx}$ levels with 0.3 dB, thus providing a useful capacity gain. The parallel use of 2 to 4 algorithms is also shown to decrease the sensitivity of the algorithms to the algorithm thresholds used, and thus increase the feasibility of the algorithms in a real world networks.

• ETRI Journal
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• v.43 no.4
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• pp.728-745
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• 2021

Carbon nanotube field-effect transistors (CNTFETs) have been widely studied as a promising technology to be included in post-complementary metal-oxide-semiconductor integrated circuits. Despite significant advantages in terms of delay and power dissipation, the fabrication process for CNTFETs is plagued by fault occurrences. Therefore, developing a fast and accurate method for estimating the reliability of CNTFET-based digital circuits was the main goal of this study. In the proposed method, effects related to faults that occur in a gate's transistors are first represented as a probability transfer matrix. Next, the target circuit's graph is traversed in topological order and the reliabilities of the circuit's gates are computed. The accuracy of this method (less than 3% reliability estimation error) was verified through various simulations on the ISCAS 85 benchmark circuits. The proposed method outperforms previous methods in terms of both accuracy and computational complexity.

• The Transactions of the Korea Information Processing Society
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• v.6 no.2
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• pp.326-334
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• 1999

There are the fault tolerance technology and the fault avoidance technology to analyze and evaluate the performance of computer system. To improve the relibility of software The N-Version Programming (NVP) technology is known to be the most objective and quantitive. However, when discrete probability distribution is used as estimation model, the values of it's component reliability should be same. In this paper, to resolve this problem, we adapted the genetic algorithms to NVP technology and implement the optimized simulate. and the results were analyzed and estimated. Through this study, we could optimize the reliability of each component and estimate the optimum count in the system reliability.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.8
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• pp.471-480
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• 2003

The design of a measurement system to perform Harmonic State Estimation (HSE) is a very complex problem. Among the reasons for its complexity are the system size, conflicting requirements of estimator accuracy, reliability in the presence of transducer noise and data communication failures, adaptability to change in the network topology and cost minimization. In particular, the number of harmonic instruments available is always limited. Therefore, a systematic procedure is needed to design the optimal placement of measurement points. This paper presents a new HSE algorithm which is based on an optimal placement of measurement points using Genetic Algorithms (GAs) which is widely used in areas such as: optimization of the objective function, learning of neural networks, tuning of fuzzy membership functions, machine learning, system identification and control. This HSE has been applied to the Simulation Test Power System for the validation of the new HSE algorithm. The study results have indicated an economical and effective method for optimal placement of measurement points using Genetic Algorithms (GAs) in the Harmonic State Estimation (HSE).

• Proceedings of the KIEE Conference
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• 1990.11a
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• pp.239-243
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• 1990

This paper presents a algorithm to improve accuracy and reliability in state estimation of contaminated bad data. The conventional algorithms for detection of bad data confront the problems of excessive memory requirements and long computation time. In order to overcome measurement compensation approach is proposed to reduce computation time and partitioned measurement error model has the advantage of remarkable reduction in computation time and memory requirements in estimated error computation. The proposed algorithm has been tested for IEEE sample systems, which shows its applicability to on-line power systems.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.2
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• pp.144-153
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• 1991

This paper presents an algorithm to improve accuracy and reliability in the state estimation of contaminated bad data. The conventional algorithms for detection of bad data have the problems of excessive memory requirements and long computation time. In order to overcome these problems, a measurement compensation approach is proposed to reduce computation time, and the partitioned measurement error model has the advantage of remarkable reduction in computation time and memory requirements in estimated error computation. The proposed algorithm has been tested for IEEE sample systems, which shows its applicability to on-line power systems.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.06a
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• pp.90-91
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• 2018

We propose a novel high dynamic range (HDR) video synthesis algorithm using alternatively exposed low dynamic range (LDR) videos. We first develop a superpixel-based illumination invariant correspondence estimation algorithm. Then, we propose a reliability weight to further improve the quality of the synthesized HDR frame. Experimental results show that the proposed algorithm provides high-quality HDR frames compared to conventional algorithms.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.35 no.3
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• pp.238-247
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• 2007

Genetic Algorithms(GA) which searches optimum solution using natural selection and the law of heredity has been applied to learning algorithms in order to estimate performance deterioration of the aircraft gas turbine engine. The compressor, gas generator turbine and power turbine are considered for engine performance deterioration and estimation for performance deterioration of a single component at design point was conducted. As a result of that, defect diagnostics has been conducted. The input criteria for the genetic algorithm to guarantee the high stability and reliability was discussed as increasing learning data sets. As a result, the accuracy of defect estimation and diagnostics were verified with its RMS error within 3%.

• The Transactions of the Korea Information Processing Society
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• v.1 no.1
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• pp.14-22
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• 1994

This paper proposes two Bayes estimators and their evaluation algorithms of the software reliability at the end testing stage in the Smith's Bayesian software reliability growth model under the data prior distribution BE(a, b), which is more general than uniform distribution, as a class of prior information. We consider both a squared-error loss function and the Harris loss function in the Bayesian estimation procedures. We also compare the MSE performances of the Bayes estimators and their algorithms of software reliability using computer simulations. And we conclude that the Bayes estimator of software reliability under the Harris loss function is more efficient than other estimators in terms of the MSE performances as a is larger and b is smaller, and that the Bayes estimators using the beta prior distribution as a conjugate prior is better than the Bayes estimators under the uniform prior distribution as a noninformative prior when a＞b.

• Structural Engineering and Mechanics
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• v.78 no.2
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• pp.209-218
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• 2021

For engineering, there are two major challenges in reliability analysis. First, to ensure the accuracy of simulation results, mechanical products are usually defined implicitly by complex numerical models that require time-consuming. Second, the mechanical products are fortunately designed with a large safety margin, which leads to a low failure probability. This paper proposes an efficient and high-precision adaptive active learning algorithm based on the Kriging surrogate model to deal with the problems with low failure probability and time-consuming numerical models. In order to solve the problem with multiple failure regions, the adaptive kernel-density estimation is introduced and improved. Meanwhile, a new criterion for selecting points based on the current Kriging model is proposed to improve the computational efficiency. The criterion for choosing the best sampling points considers not only the probability of misjudging the sign of the response value at a point by the Kriging model but also the distribution information at that point. In order to prevent the distance between the selected training points from too close, the correlation between training points is limited to avoid information redundancy and improve the computation efficiency of the algorithm. Finally, the efficiency and accuracy of the proposed method are verified compared with other algorithms through two academic examples and one engineering application.