• Journal of Korean Institute of Industrial Engineers
• /
• v.5 no.1
• /
• pp.53-57
• /
• 1979

The concept of AOQL in designing Dodge's continuous sampling plans is modified to include probabilistic consideration reflecting the prior knowledge about the process average fraction defectives, and a new design criterion called AOQL, which eliminates some of the drawbacks of the AOQL criterion is proposed. AOQL, approach provides more economical sampling plans in many cases, and can be used even when only limited amount of prior information is available.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.10 no.1
• /
• pp.41-53
• /
• 1985

The influence diagram is a new conceptual tool that can be used for structuring a strategic decision problem in decision analysis. It has a graphical representation of probabilistic dependence among variables in the decision problem. In this formal procedures for constructuring the influence diagram and for translating it into the corresponding decision tree are studied. An example that shows the power of the infuence diagram is shown.

• Journal of the Korean Society for information Management
• /
• v.7 no.1
• /
• pp.68-78
• /
• 1990

This paper shows the fuzzy reasoning process that is specifically designed to deal wit the inexactness or fuzziness in the expert systems. The impact of overall fuzzy reasoning reviewed when knowledge with certainty is provided. Also, the example of fuzzy reazoning used at probabilistic inference is presented.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 2003.10a
• /
• pp.271-276
• /
• 2003

In this study, we compared backpropagation neural network(BPNN) with probabilistic neural network(PNN) as shape recognition algorithm of welding flaws. For this purpose, variables are applied the same to two algorithm. Here, feature variable is composed of time domain signal itself and frequency domain signal itself, Through this process, we comfirmed advantages/disadvantages of two algorithms and identified application methods of two algorithms.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 2004.10a
• /
• pp.280-285
• /
• 2004

In this study, we made a comparative study of backpropagation neural network and probabilistic neural network and bayesian classifier and perceptron as shape recognition algorithm of welding flaws. For this purpose, variables are applied the same to four algorithms. Here, feature variable is composed of time domain signal itself and frequency domain signal itself. Through this process, we comfirmed advantages/disadvantages of four algorithms and identified application methods of four algorithms.

• Journal of the Korean Statistical Society
• /
• v.26 no.4
• /
• pp.521-530
• /
• 1997

The new Laplace autoregressive model of order 2-NLAR92) studied by Dewald and Lewis (1985) is extended to the p-th order model-NLAR(p). A necessary and sufficient condition for the existence of an innovation sequence and a stationary ergodic NLAR(p) model is obtained. It is shown that the distribution of the innovation sequence is given by the probabilistic mixture of independent Laplace distributions and a degenrate distribution.

• Proceedings of the Earthquake Engineering Society of Korea Conference
• /
• 1998.04a
• /
• pp.123-130
• /
• 1998

The probability of unseating failure of the bridge spans under earthquakes is investigated. Seismic excitations are simulated as nonstationary processes by combining a stationary process and an intensity function. For computational convenience, a simplified single-degree-of-freedom model is adopted, which retains the dynamic characteristics of the original brige motion in concern. The time history analysis for the developed single degree-of-freedom model are carried out to evaluate the response processes, and the probabilistic characteristics of response displacements are evaluated. The reliability analysis of the bridge against the unseating failure is performed with the statistical information of the maximum displacements of responses.

• Journal of the Korean Statistical Society
• /
• v.29 no.1
• /
• pp.1-8
• /
• 2000

A spectrum minimizing the frequency-domain Kullback-Leibler information number has been proposed and used to modify a spectrum estimate. Some numerical examples have illustrated the KL spectrum estimate is superior to the initial estimate, i.e., the autocovariances obtained by the inverse Fourier transformation of the KL spectrum estimate are closer to the sample autocovariances of the given observations than those of the initial spectrum estimate. Also, it has been shown that a Gaussian autoregressive process associated with the KL spectrum is the closest in the timedomain Kullback-Leibler sense to a Gaussian white noise process subject to given autocovariance constraints. In this paper a corresponding conditional probability theorem is presented, which gives another rationale to the KL spectrum.

• Journal of Institute of Control, Robotics and Systems
• /
• v.9 no.2
• /
• pp.168-175
• /
• 2003

A new algorithm based on Genetic Algorithms is proposed f3r solving process optimization problems formulated in MINLP, GDP and hybrid MINLP/GDP. This work is focused especially on the design of the Genetic Algorithm suitable to handle disjunctive programming with the same level of MINLP handling capability. Hybridization with the Simulated Annealing is experimented and many heuristics are adopted. Real and binary coded Genetic Algorithm initiates the global search in the entire search space and at every stage Simulated Annealing makes the candidates to climb up the local hills. Multi-Niche Crowding method is adopted as the multimodal function optimization technique. and the adaptation of probabilistic parameters and dynamic penalty systems are also implemented. New strategies to take the logical variables and constraints into consideration are proposed, as well. Various test problems selected from many fields of process systems engineering are tried and satisfactory results are obtained.

• Journal of Biomedical Engineering Research
• /
• v.13 no.1
• /
• pp.1-8
• /
• 1992

In the field of prosthesis arm control, the pattern classification of the EMG signal is a required basis process and also the estimation of force from collected EMG data is another necessary duty. But unfortunately, what we've got is not real force but an EMG signal which contains the information of force. This is the reason why we estimate the force from the EMG data. In this paper, when we handle the EMG signal to estimate the force, spatial prewhitening process is applied from which the spatial correlation between the channels are removed. And after the orthogonal transformation which is used in the force estimation process, the transformed signal Is inputed into the probabilistic model for pattern classification. To verify the different results of the multiple channels, SNR(signal to noise ratio) function is introduced.