• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.2A
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• pp.293-300
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• 2006

There is a high degree of uncertainty in measurements of the thickness or the loss of thickness of corroded elements. Generally the thickness of corroded elements varies from one location of the element to another depending on the degree of corrosion, which makes the safety assessment difficult. Therefore, a procedure for safety assessment of corrosion- damaged steel structures using an imprecise reliability is proposed in this paper. The proposed safety assessment procedure using the imprecise reliability was also applied to a cable-stayed bridge in Korea to demonstrate its effectiveness and applicability. Since there is a large variation in measurements of the thickness of corroded elements, the thickness of corroded elements was considered as the imprecise element. This variation was found to be directly related to the degree of corrosion. Therefore, the variation increases as the degree of corrosion increases. Based on the comparative observations between the conventional reliability and the imprecise reliability, it is suggested that the imprecise reliability analysis derived based on the subjective or statistical judgment of conditional independence could be successfully utilized for the risk or safety assessment of corrosion-damaged structures.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2004.04a
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• pp.267-274
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• 2004

Since there is a large variation in measurements of the thickness of corroded elements, the thickness of corroded elements are considered as imprecise elements. There is also a considerable degree of uncertainty in a visual assessment of thickness loss. The remaining thickness of a severly corroded element may be represented by an imprecise which expresses the range over which there is uncertainty about the thickness. Therefore, the objective of this paper is to propose a new methodology to safety assessment using imprecise reliability into conventional safety assessment frameworks. For this purpose, this study presents a safety assessment model using Imprecise reliability for large civil structures and demonstrates the applicability of the approach to cable-stayed bridge projects.

• Proceedings of the KIEE Conference
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• summer
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• pp.348-350
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• 2004

This paper presents the scheme for reliability improvement by dispersed generation system (US) installation and operation in distribution systems. The objective functions such as power losses cost, operation cost of DGS, power buy cost and interruption cost are minimized for reliability improvement. The original objective functions and constraints are transformed into the equivalent multiple objective functions with fuzzy sets to evaluate their imprecise nature. The several indices for reliability evaluation are improved by dispersed generation system installation.

• Transactions of the Korean hydrogen and new energy society
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• v.19 no.5
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• pp.453-459
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• 2008

In general, reliability of AHP depends on pairwise comparison of evaluators. In addition, human judgment on the importance of alternatives or criteria is always imprecise and vague. The Fuzzy AHP technique is suggested and used widely recently. In this paper, we prioritize future energy technology development for well focused R&D. We selected 3 criteria and 10 sub-criteria. According to the result in this study, the most important sub-criterion is the precedence over other competitive technology, the second is the possibility for fundamental technology acquisition, and the third is the improvement energy efficiency. The other side, the lowest important sub-criterion is the technology level compared with advanced countries.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.30 no.1
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• pp.89-95
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• 2007

In general, reliability of AHP(Analytic Hierarchy Process) depends on pairwise comparison of evaluators. In addition, human judgment on the importance of alternatives or criteria is always imprecise and vague. To cope with these shortcomings, Fuzzy AHP is suggested and used widely recently. But in Fuzzy AHP, it cannot deal with the evaluator's various attitudes towards risk and confidence owing to evaluator's different expertise and experience. This paper proposes a method for consideration of evaluator's confidence and risk attitude in Fuzzy AHP. And suggested methods are applied various scenarios to verify the meaningfulness. The result shows that the priority of alternatives can be change through the consideration of evaluator's confidence and risk attitude.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.3965-3982
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• 2015

Event detection is one of the key issues in many wireless sensor network (WSN) applications. The uncertainties that are derived from the instability of sensor node, measurement noise and incomplete sampling would influence the performance of event detection to a large degree. Many of the present researches described the sensor readings with crisp values, which cannot adequately handle the uncertainties inhered in the imprecise sensor readings. In this paper, a fault-tolerant event detection algorithm is proposed based on Dempster-Shafer (D-S) theory (also called evidence theory). Instead of crisp values, all possible states of the event are represented by the Basic Probability Assignment (BPA) functions, with which the output of each sensor node are characterized as weighted evidences. The combination rule was subsequently applied on each sensor node to fuse the evidences gathered from the neighboring nodes to make the final decision on whether the event occurs. Simulation results show that even 20% nodes are faulty, the accuracy of the proposed algorithm is around 80% for event region detection. Moreover, 97% of the error readings have been corrected, and an improved detection capability at the boundary of the event region is gained by 75%. The proposed algorithm can enhance the detection accuracy of the event region even in high error-rate environment, which reflects good reliability and robustness. The proposed algorithm is also applicable to boundary detection as it performs well at the boundary of the event.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.65-65
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• 2002

In remote sensing, images are acquired over the same area by sensors of different spectral ranges (from the visible to the microwave) and/or with different number, position, and width of spectral bands. These images are generally partially redundant, as they represent the same scene, and partially complementary. For many applications of image classification, the information provided by a single sensor is often incomplete or imprecise resulting in misclassification. Fusion with redundant data can draw more consistent inferences for the interpretation of the scene, and can then improve classification accuracy. The common approach to the classification of multisensor data as a data fusion scheme at pixel level is to concatenate the data into one vector as if they were measurements from a single sensor. The multiband data acquired by a single multispectral sensor or by two or more different sensors are not completely independent, and a certain degree of informative overlap may exist between the observation spaces of the different bands. This dependence may make the data less informative and should be properly modeled in the analysis so that its effect can be eliminated. For modeling and eliminating the effect of such dependence, this study employs a strategy using self and conditional information variation measures. The self information variation reflects the self certainty of the individual bands, while the conditional information variation reflects the degree of dependence of the different bands. One data set might be very less reliable than others in the analysis and even exacerbate the classification results. The unreliable data set should be excluded in the analysis. To account for this, the self information variation is utilized to measure the degrees of reliability. The team of positively dependent bands can gather more information jointly than the team of independent ones. But, when bands are negatively dependent, the combined analysis of these bands may give worse information. Using the conditional information variation measure, the multiband data are split into two or more subsets according the dependence between the bands. Each subsets are classified separately, and a data fusion scheme at decision level is applied to integrate the individual classification results. In this study. a two-level algorithm using hierarchical clustering procedure is used for unsupervised image classification. Hierarchical clustering algorithm is based on similarity measures between all pairs of candidates being considered for merging. In the first level, the image is partitioned as any number of regions which are sets of spatially contiguous pixels so that no union of adjacent regions is statistically uniform. The regions resulted from the low level are clustered into a parsimonious number of groups according to their statistical characteristics. The algorithm has been applied to satellite multispectral data and airbone SAR data.