• Journal of the Ergonomics Society of Korea
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• v.30 no.1
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• pp.137-150
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• 2011

Nationally and internationally reported statistics on marine accidents show that 80% or more of all marine accidents are caused fully or in part by human error. According to the statistics of marine accident causes from Korean Maritime Safety Tribunal(KMST), operating errors are implicated in 78.7% of all marine accidents that occurred from 2002 to 2006. In the case of the collision accidents, about 95% of all collision accidents are caused by operating errors, and those human error related collision accidents are mostly caused by failure of maintaining proper lookout and breach of the regulations for preventing collision. One way of reducing the probability of occurrence of the human error related marine accidents effectively is by investigating and understanding the role of the human elements in accident causation. In this paper, causal factors/root causes classification systems for marine accident investigation were reviewed and some typical human error analysis methods used in shipping industry were described in detail. This paper also proposed a human error analysis method that contains a cognitive process model, a human error analysis technique(Maritime HFACS) and a marine accident causal chains, and then its application to the actual marine accident was provided as a case study in order to demonstrate the framework of the method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.11
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• pp.1758-1764
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• 2012

A large number of wire rope has been used in various inderstiries as Cranes and Elevators from expanding the scale of the industrial market. But now, the management of wire rope is used as manually operated by rope replacement from over time or after the accident.It is caused to major accidents as well as economic losses and personal injury. Therefore its time to need periodic fault diagnosis of wire rope or supply of real-time monitoring system. Currently, there are several methods has been reported for fault diagnosis method of the wire rope, to find out the feature point from extracting method is becoming more common compared to time wave and model-based system. This method has implemented a deterministic modeling like the observer and neural network through considering the state of the system as a deterministic signal. However, the out-put of real system has probability characteristics, and if it is used as a current method on this system, the performance will be decreased at the real time. And if the random noise is occurred from unstable measure/experiment environment in wire rope system, diagnostic criterion becomes unclear and accuracy of diagnosis becomes blurred. Thus, more sophisticated techniques are required rather than deterministic fault diagnosis algorithm. In this paper, we developed the fault diagnosis of the wire rope using probability density estimation techniques algorithm. At first, The steady-state wire rope fault signal detection is defined as the probability model through probability distribution estimate. Wire rope defects signal is detected by a hall sensor in real-time, it is estimated by proposed probability estimation algorithm. we judge whether wire rope has defection or not using the error value from comparing two probability distribution.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.686-691
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• 1994

For evaluating the response fluctuation of the actual environmental acoustic system excited by arbitrary random inputs, it is important to predict a whole probability distribution form closely connected with evaluation indexes Lx, Leq and so on. In this paper, a new type evaluation method is proposed by introducing three functional models matched to the prediction of the response probability distribution from a problem-oriented viewpoint. Because of the positive variable of the sound intensity, the response probability density function can be reasonably expressed theoretically by a statistical Laguerre expansion series form. The relationship between input and output is described by the regression relationship between the distribution parameters(containing expansion coefficients of this expression) and the stochastic input. These regression functions are expressed in terms of the orthogonal series expansion and their parameters are determined based on the least-squares error criterion and the measure of statistical independency.

• Journal of Communications and Networks
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• v.15 no.2
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• pp.132-141
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• 2013

In this paper, we present diversity coded orthogonal frequency division multiplexing (DC-OFDM), an approach to maximize the probability of successful reception and increase the reliability of OFDM-based systems through diversity coding. We focus on the application of DC-OFDM to vehicular networks based on IEEE 802.11p technology and analyze the performance improvement using this new technology. It is shown that DC-OFDM significantly improves the performance of vehicular ad hoc networks in terms of throughput and the expected number of correctly received symbols.

• Journal of Communications and Networks
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• v.9 no.1
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• pp.67-74
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• 2007

The performance of dual diversity receivers operating over correlated Ricean fading channels is analyzed. Using a previously derived rapidly converging infinite series representation for the bivariate Ricean probability density function, analytical expressions for the statistics of dual-branch selection combining, maximal-ratio combining, and equal-gain combining output signal-to-noise ratio (SNR) are derived. These expressions are employed to obtain novel analytical formulae for the average output SNR, amount of fading, average bit error probability, and outage probability. The proposed mathematical analysis is used to study various novel performance evaluation results with parameters of interest the fading severity, average input SNRs, and the correlation coefficient. The series convergence rate is also examined verifying the fast convergence of the analytical expressions. The accuracy of most of the theoretical performance evaluation results are validated by means of computer simulations.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.7
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• pp.1007-1013
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• 2012

Modeling of photovoltaic power systems is significant to analytically predict its dynamics in practical applications. This paper presents a novel modeling algorithm of such system by using probability and statistic theories. We first establish a linear model basically composed of Fourier parameter sets for mapping the input/output variable of photovoltaic systems. The proposed model includes solar irradiation and ambient temperature of photovoltaic modules as an input vector and the inverter power output is estimated sequentially. We deal with these measurements as random variables and derive a parameter learning algorithm of the model in terms of statistics. Our learning algorithm requires computation of an expectation and joint expectation against solar irradiation and ambient temperature, which are analytically solved from the integral calculus. For testing the proposed modeling algorithm, we utilize realistic measurement data sets obtained from the Seokwang Solar power plant in Youngcheon, Korea. We demonstrate reliability and superiority of the proposed photovoltaic system model by observing error signals between a practical system output and its estimation.

• Communications for Statistical Applications and Methods
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• v.11 no.1
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• pp.31-47
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• 2004

The history of statistics from the mid-seventeenth to the early twentieth century is considered and a scheme of periodization is proposed. In the first period(1650-1750), named 'the age of probability' in this paper, concept of probability emerged, and in the second period(1750-1820), named 'the age of error theory', statistical techniques such as the least square method are developed by astronomers and geodesists. Their techniques are supported theoretically by mathematicians like Laplace and Gauss in that period. The third period(1820-1880) is called 'the age of statistics(as a plural noun)' since statistical data played prominent roles in social sciences such as sociology, psychology. Finally the last period(1880- ), called 'the age of statistics(as a singular noun)', the discipline of statistics came to maturity both in theory and application.

• Journal of Communications and Networks
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• v.13 no.4
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• pp.393-399
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• 2011

We evaluate the link-level performance of cooperative multi-hop relaying networks with an maximum distance separable (MDS) code. The effect of the code on the link-level performance at the destination is investigated in terms of the outage probability and the spectral efficiency. Assuming a simple topology, we construct an absorbing Markov chain. Numerical results indicate that significant improvement can be achieved by incorporating an MDS code. MDS codes successfully facilitate recovery of the message block at a relaying node due to powerful error-correcting capability, so that it can reduce the outage probability. Furthermore, we evaluate the average number of hops where the message block can be delivered.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.1
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• pp.29-35
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• 2012

This paper introduces an adaptive control method of strong mutation rate and probability for queen-bee genetic algorithms. Although the queen-bee genetic algorithms have shown good performances, it had a critical problem that the strong mutation rate and probability should be selected by a trial and error method empirically. In order to solve this problem, we employed the measure of convergence and used it as a control parameter of those. Experimental results with four function optimization problems showed that our method was similar to or sometimes superior to the best result of empirical selections. This indicates that our method is very useful to practical optimization problems because it does not need time consuming trials.

• Journal of the Korean Operations Research and Management Science Society
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• v.9 no.2
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• pp.23-27
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• 1984

We Observe a sequence of i. i. d random variables with density f or g. Only if g is true we should stop the process. Hence. the testing problem is completely described by a stopping time. Among all stopping times with error probability of first kind not exceeding a given bound, the one-sided sequential probability ratio test has smallest expected sample size if g is true. Moreover, the generalized one-sided SPRT has smallest expected sample size for g in the class of stopping times with expected sample size under f not falling below a given bound.