• Journal of Ocean Engineering and Technology
• /
• v.9 no.1
• /
• pp.132-141
• /
• 1995

The purpose of the present study is to investigate the statistical characteristics of m and C in the fatigue crack propagation law, da/dN=C(.DELTA.K)/sup m/ and to studies on the randomness of fatigue crack propagation and retardation behavior. Fatigue tests were perfomed on 32 CT specimens of SPV50 steel under the same one condition. First, the value of m and C were determined for each specimen, and all the data were analyzed statistically. second, the material's resistance to fatigue crack propagation is modeled as a stchastic process, which varies randomly along the crack path. The statistical analysis of the material resistance is performed with the data obtained by constant load controlled tests. Finally, retardation behavior was examined experimentally by using a CT specimen, and a retardation parameters were analyzed statistically.

• Journal of Biomedical Engineering Research
• /
• v.9 no.1
• /
• pp.101-108
• /
• 1988

In this paper the power spectrum of background EEG is estimated by the LMS PHD based on least mean square. At the power spectrum estimatiom, the stocastic process of background EEG is assumed to consist of the nonharmonic sinusoid and the white noise. In the LMS PHD the model parameters are obtained by the least mean square at optimal order which is obtained from the fact that the eigenvalue's fluctuation of autocorrelation matrix of the normal back-ground EEG is smaller at some order than at other order when the power spectrum of background EEG is esitmated by PHD. The optimal order of this model is the 6-th order when the eigenvalue's fluctuation of autocorrelation matrix of background EEG is considered. The estimation results are with compared the results from the Maximum Entropy Spectral Estimation and Pisarenko Harmonic Decomposition. From the comparison results. The LMS PHD is possible to estimate the power spectrum of background EEG.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.3 no.1
• /
• pp.81-91
• /
• 1978

Decision models are an attempt to reduce uncertainty in the decision making process. The models describe the relationships of variables and given proper input data generate solutions to managerial problems. These solutions may not be answers to the problems for one of two reasons. First, the data input into the model may not be consistant with the underlying assumptions of the model being used. Frequently parameters are assumed to be deterministic when in fact they are probabilistic in nature. The second failure is that often the decision maker recognizes that the data available are not appropriate for the model being used and begins to collect the required data. By the time these data has been compiled the solution is no longer an answer to the problem. This relates to the timeliness of decision making. The authors point out throught the use of an illustrative problem that stocastic models are well developed and that they do not suffer from any lack of mathematical exactiness. The primary problem is that generally accepted procedures for data generation are historical in nature and not relevant for probabilistic decision models. The authors advocate that management information system designers and accountants must become more familiar with these decision models and the input data required for their effective implementation. This will provide these professionals with the background necessary to generate data in a form that makes it relevant and timely for the decision making process.