• Journal of Korea Society of Digital Industry and Information Management
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• v.9 no.3
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• pp.21-30
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• 2013

Powell-Miller theory is a good method to express or treat incorrect information. But it has limitation that requires too much time to apply to actual situation because computational complexity increases in exponential and functional way. Accordingly, there have been several attempts to reduce computational complexity but side effect followed - certainty factor fell. This study suggested expanded Approximation Algorithm. Expanded Approximation Algorithm is a method to consider both smallest supersets and largest subsets to expand basic space into a space including inverse set and to reduce Approximation error. By using expanded Approximation Algorithm suggested in the study, basic probability assignment function value of subsets was alloted and added to basic probability assignment function value of sets related to the subsets. This made subsets newly created become Approximation more efficiently. As a result, it could be known that certain function value which is based on basic probability assignment function is closely near actual optimal result. And certainty in correctness can be obtained while computational complexity could be reduced. by using Algorithm suggested in the study, exact information necessary for a system can be obtained.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.3
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• pp.465-470
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• 2013

Dempster-Shafer Evidence Theory is available for multi-sensor data fusion. Basic Probability Assignment is essential for multi-sensor data fusion using Dempster-Shafer Theory. In this paper, we proposed a novel method of BPA calculation with signal assessment. We took notice of the signal that reported from the sensor mote at the time slot. We assessed the variation rate of the reported signal from the terminal. The trend of variation implies significant component of the context. We calculated the variation rate of signal for reveal the relation of the variation and the context. We could reach context inference with BPA that calculated with the variation rate of signal.

• The KIPS Transactions:PartC
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• v.13C no.2 s.105
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• pp.167-170
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• 2006

Most wavelength assignment algorithms assume that every node has infinite ADMs and I/O blocking is excluded. Furthermore wavelengths continue to occur intermittently in the network using existing algorithms. This can result in increased link blocking under dynamic traffic circumstances. In audition, it can create time-complexity problems such as O(W $N^3$) in the worst case scenario. In this paper, we propose a new wavelength assignment algorithm which has time-complexity of O(W $N^2$) and considers both I/O and link blocking. The basic idea is to maximize the number of links where wavelengths will be assigned continuously after present assignment. As a result, the number of intermittent wavelengths is minimized, and blocking probabilities are decreased.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.7
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• pp.1732-1743
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• 1998

In this paper, we have proposed a channel assignment shceme for the multiple traffic class environment where the traffic volume varies with time. Our basic idea is to assign radio channels considering the characteristics of each traffic class and mobile speed and change the number of allocated channels for each traffic class according to vaiation of traffic volume. To evalute the perfomance of our proposed algorithms, we conducted analyical and simulation studies. We derived new call blocking probability and hand-off failure probability of the proposed scheme analytically. The simulatioh results show that our proposed algorithms can grantee GOS of each traffic class and provide a fairness between the new call and the hand-off call.

• International Journal of Quality Innovation
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• v.5 no.1
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• pp.43-51
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• 2004

During process design or process optimization, it is quite common for experimenters to find optimum operating conditions for several responses simultaneously. The traditional multiresponse surfaces optimization methods do not consider the uncertain relationship among these responses sufficiently. For this reason, the authors propose an optimization method based on evidential reasoning theory by Dempster and Shafer. By maximizing the basic probability assignment function, which indicates the degree of belief that certain operating condition is the solution of this multiresponse surfaces optimization problem, the desirable operating condition can be found.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.5
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• pp.462-469
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• 2023

In this study, we propose a method for evaluating the risk of collision between ships to support determination on the risk of collision in a situation in which ships encounter each other and to prevent collision accidents. Because several uncertainties are involved in the navigation of a ship, must be considered when evaluating the risk of collision. We apply the Dempster-Shafer theory to manage this uncertainty and evaluate the collision risk of each target vessel in real time. The distance at the closest point approach (DCPA), time to the closest point approach (TCPA), distance from another vessel, relative bearing, and velocity ratio are used as evaluation factors for ship collision risk. The basic probability assignments (BPAs) calculated by membership functions for each evaluation factor are fused through the combination rule of the Dempster-Shafer theory. As a result of the experiment using automatic identification system (AIS) data collected in situations where ships actually encounter each other, the suitability of evaluation was verified. By evaluating the risk of collision in real time in encounter situations between ships, collision accidents caused by human errora can be prevented. This is expected to be used for vessel traffic service systems and collision avoidance systems for autonomous ships.

• Journal of Korean Society of Transportation
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• v.21 no.2
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• pp.95-105
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• 2003

Because the basic assumptions of deterministic user equilibrium assignment that all network users have perfect information of network condition and determine their routes without errors are known to be unrealistic, several stochastic assignment models have been proposed to relax this assumption. However. it is not easy to solve such stochastic assignment models due to the probability distribution they assume. Also. in order to avoid all path enumeration they restrict the number of feasible path set, thereby they can not preciously explain the travel behavior when the travel cost is varied in a network loading step. Another problem of the stochastic assignment models is stemmed from that they use heuristic approach in attaining optimal moving size, due to the difficulty for evaluation of their objective function. This paper presents a logit-based stochastic assignment model and its solution algorithm to cope with the problems above. We also provide a stochastic user equilibrium condition of the model. The model is based on path where all feasible paths are enumerated in advance. This kind of method needs a more computing demand for running the model compared to the link-based one. However, there are same advantages. It could describe the travel behavior more exactly, and too much computing time does not require than we expect, because we calculate the path set only one time in initial step Two numerical examples are also given in order to assess the model and to compare it with other methods.

• International journal of advanced smart convergence
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• v.8 no.1
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• pp.133-140
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• 2019

In this paper, we present an algorithm for adjusting degree of belief for consistency on the evidence dependency network where various sets of evidence support different sets of hypotheses. It is common for experts to assign higher degree of belief to a hypothesis when there is more evidence over the hypothesis. Human expert without knowledge of uncertainty handling may not be able to cope with how evidence is combined to produce the anticipated belief value. Belief in a hypothesis changes as a series of evidence is known to be true. In non-monotonic reasoning environments, the belief retraction method is needed to clearly deal with uncertain situations. We create evidence dependency network from rules and apply the evidence retraction algorithm to refine belief values on the hypothesis set. We also introduce negative belief values to reflect the reverse effect of evidence combination.

• Korean Journal of Remote Sensing
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• v.28 no.6
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• pp.611-622
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• 2012

This study proposed two multisensor fusion methods for segment-based image classification utilizing a region-growing segmentation. The proposed algorithms employ a Gaussian-PDF measure and an evidential measure respectively. In remote sensing application, segment-based approaches are used to extract more explicit information on spatial structure compared to pixel-based methods. Data from a single sensor may be insufficient to provide accurate description of a ground scene in image classification. Due to the redundant and complementary nature of multisensor data, a combination of information from multiple sensors can make reduce classification error rate. The Gaussian-PDF method defines a regional measure as the PDF average of pixels belonging to the region, and assigns a region into a class associated with the maximum of regional measure. The evidential fusion method uses two measures of plausibility and belief, which are derived from a mass function of the Beta distribution for the basic probability assignment of every hypothesis about region classes. The proposed methods were applied to the SPOT XS and ENVISAT data, which were acquired over Iksan area of of Korean peninsula. The experiment results showed that the segment-based method of evidential measure is greatly effective on improving the classification via multisensor fusion.

• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.891-893
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• 1997

In this paper an intelligent power transformer protective relaying algorithm based on Fuzzy Decision-Making is presented. The introduced protection algorithm contains several internal fuzzy rule-bases including bpa(Basic Probability Assignment: m) which are subject to off-line pre-installation by the analysis of the transformer transient characteristics for detecting the internal fault. Dempster-Shafer's rule of combination is used for the inference method with rules to decide the situation of a transformer, The proposed algorithm immunes to the saturation of transformer, inrush conditions, over excitation, and external fault. The included results of testing show practically sufficient sensitivity and selectivity of the proposed algorithm.